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User-Independent Gaze Estimation by Extracting Pupil Parameter and Its Mapping to the Gaze Angle

Sang Yoon Han, Nam Ik Cho

Auto-TLDR; Gaze Point Estimation using Pupil Shape for Generalization

Abstract Slides Poster

Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.

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Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets

Yiwei Bao, Yihua Cheng, Yunfei Liu, Feng Lu

Auto-TLDR; Adaptive Feature Fusion Network for Multi-stream Gaze Estimation in Mobile Tablets

Abstract Slides Poster Similar

Recently, many multi-stream gaze estimation methods have been proposed. They estimate gaze from eye and face appearances and achieve reasonable accuracy. However, most of the methods simply concatenate the features extracted from eye and face appearance. The feature fusion process has been ignored. In this paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We stack two-eye feature maps and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features based on different eye features. Meanwhile, we also propose Adaptive Group Normalization to recalibrate eye features with the guidance of face appearance characteristics. Extensive experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.

Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep Learning

Soumil Chugh, Braiden Brousseau, Jonathan Rose, Moshe Eizenman

Auto-TLDR; A Fully Convolutional Neural Network for Corneal Reflection Detection and Matching in Extended Reality Eye Tracking Systems

Abstract Slides Poster Similar

Eye tracking systems that estimate the point-of-gaze are essential in extended reality (XR) systems as they enable new interaction paradigms and technological improvements. It is important for these systems to maintain accuracy when the headset moves relative to the head (known as device slippage) due to head movements or user adjustment. One of the most accurate eye tracking techniques, which is also insensitive to shifts of the system relative to the head, uses two or more infrared (IR) light emitting diodes to illuminate the eye and an IR camera to capture images of the eye. An essential step in estimating the point-of-gaze in these systems is the precise determination of the location of two or more corneal reflections (virtual images of the IR-LEDs that illuminate the eye) in images of the eye. Eye trackers tend to have multiple light sources to ensure at least one pair of reflections for each gaze position. The use of multiple light sources introduces a difficult problem: the need to match the corneal reflections with the corresponding light source over the range of expected eye movements. Corneal reflection detection and matching often fail in XR systems due to the proximity of camera and steep illumination angles of light sources with respect to the eye. The failures are caused by corneal reflections having varying shape and intensity levels or disappearance due to rotation of the eye, or the presence of spurious reflections. We have developed a fully convolutional neural network, based on the UNET architecture, that solves the detection and matching problem in the presence of spurious and missing reflections. Eye images of 25 people were collected in a virtual reality headset using a binocular eye tracking module consisting of five infrared light sources per eye. A set of 4,000 eye images were manually labelled for each of the corneal reflections, and data augmentation was used to generate a dataset of 40,000 images. The network is able to correctly identify and match 91% of corneal reflections present in the test set. This is comparable to a state-of-the-art deep learning system, but our approach requires 33 times less memory and executes 10 times faster. The proposed algorithm, when used in an eye tracker in a VR system, achieved an average mean absolute gaze error of 1°. This is a significant improvement over the state-of-the-art learning-based XR eye tracking systems that have reported gaze errors of 2-3°.

Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

Shigang Li

Auto-TLDR; Gaze Point Estimation from a Spherical Image from Facial Landmarks

Abstract Slides Poster Similar

From a spherical image, a gaze point, instead of gaze vectors, can be estimated directly because a remote spherical camera can observe a user's face and a gaze target simultaneously. This paper investigates the problem of estimating a gaze point in a spherical image from facial landmarks. In contrast with the existing methods which usually assume gaze points move on a narrow plane, the proposed method can cope with the situation where gaze points vary in depth for a relatively wide field of view. As shown in the results of comparative experiments, we find the orthogonal coordinates of facial landmarks on a unit sphere is a reasonable representation in comparison with spherical polar coordinates; the cues of head pose is helpful to improve the accuracy of gaze points. Consequently, the proposed method achieves a performance on the accuracy of gaze points estimation which is comparable to the state of the art methods.

Classifying Eye-Tracking Data Using Saliency Maps

Shafin Rahman, Sejuti Rahman, Omar Shahid, Md. Tahmeed Abdullah, Jubair Ahmed Sourov

Auto-TLDR; Saliency-based Feature Extraction for Automatic Classification of Eye-tracking Data

Abstract Slides Poster Similar

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2],[4], [5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction

Wolfgang Fuhl, Yao Rong, Enkelejda Kasneci

Auto-TLDR; Semantic Segmentation of Eye Tracking Data with Fully Convolutional Neural Networks

Abstract Slides Poster Similar

In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with a variational auto-encoder to generate eye movement data. The first improvement of our approach is that no input window is necessary, due to the use of fully convolutional networks and therefore any input size can be processed directly. The second improvement is that the used and generated data is raw eye tracking data (position X, Y and time) without preprocessing. This is achieved by pre-initializing the filters in the first layer and by building the input tensor along the z axis. We evaluated our approach on three publicly available datasets and compare the results to the state of the art.

Exposing Deepfake Videos by Tracking Eye Movements

Meng Li, Beibei Liu, Yujiang Hu, Yufei Wang

Auto-TLDR; A Novel Approach to Detecting Deepfake Videos

Abstract Slides Poster Similar

It has recently become a major threat to the public media that fake videos are rapidly spreading over the Internet. The advent of Deepfake, a deep-learning based toolkit, has facilitated a massive abuse of improper synthesized videos, which may influence the media credibility and human rights. A worldwide alert has been set off that finding ways to detect such fake videos is not only crucial but also urgent. This paper reports a novel approach to expose deepfake videos. We found that most fake videos are markedly different from the real ones in the way the eyes move. We are thus motivated to define four features that could well capture such differences. The features are then fed to SVM for classification. It is shown to be a promising approach that without high dimensional features and complicated neural networks, we are able to achieve competitive results on several public datasets. Moreover, the proposed features could well participate with other existing methods in the confrontation with deepfakes.

RISEdb: A Novel Indoor Localization Dataset

Carlos Sanchez Belenguer, Erik Wolfart, Álvaro Casado Coscollá, Vitor Sequeira

Auto-TLDR; Indoor Localization Using LiDAR SLAM and Smartphones: A Benchmarking Dataset

Abstract Slides Poster Similar

In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

Explainable Online Validation of Machine Learning Models for Practical Applications

Wolfgang Fuhl, Yao Rong, Thomas Motz, Michael Scheidt, Andreas Markus Hartel, Andreas Koch, Enkelejda Kasneci

Auto-TLDR; A Reformulation of Regression and Classification for Machine Learning Algorithm Validation

Abstract Slides Poster Similar

We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.

Sequential Non-Rigid Factorisation for Head Pose Estimation

Stefania Cristina, Kenneth Patrick Camilleri

Auto-TLDR; Sequential Shape-and-Motion Factorisation for Head Pose Estimation in Eye-Gaze Tracking

Abstract Slides Poster Similar

Within the context of eye-gaze tracking, the capability of permitting the user to move naturally is an important step towards allowing for more natural user interaction in less constrained scenarios. Natural movement can be characterised by changes in head pose, as well as non-rigid face deformations as the user performs different facial expressions. While the estimation of head pose within the domain of eye-gaze tracking is being increasingly considered, the face is most often regarded as a rigid body. The few methods that factor the challenge of handling face deformations into the head pose estimation problem, often require the availability of a pre-defined face model or a considerable amount of training data. In this paper, we direct our attention towards the application of shape-and-motion factorisation for head pose estimation, since this does not generally rely on the availability of an initial face model. Over the years, various shape-and-motion factorisation methods have been proposed to address the challenges of rigid and non-rigid shape and motion recovery, in a batch or sequential manner. However, the real-time recovery of non-rigid shape and motion by factorisation remains, in general, an open problem. Our work addresses this open problem by proposing a sequential factorisation method for non-rigid shape and motion recovery, which does not rely on the availability of a pre-defined face deformation model or training data. Quantitative and qualitative results show that our method can handle various non-rigid face deformations without deterioration of the head pose estimation accuracy.

A General End-To-End Method for Characterizing Neuropsychiatric Disorders Using Free-Viewing Visual Scanning Tasks

Hong Yue Sean Liu, Jonathan Chung, Moshe Eizenman

Auto-TLDR; A general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features

Abstract Slides Poster Similar

The growing availability of eye-gaze tracking technology has allowed for its employment in a wide variety of applications, one of which is the objective diagnosis and monitoring of neuropsychiatric disorders from features of attentional bias extracted from visual scanning patterns. Current techniques in this field are largely comprised of non-generalizable methodologies that rely on domain expertise and study-specific assumptions. In this paper, we present a general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features to classify between subject groups with standard machine learning techniques. During the free-viewing task, subjects view sets of slides with thematic images while their visual scanning patterns (sets of ordered fixations) are monitored by an eye-tracking system. We encode fixations into relative visual attention maps (RVAMs) to describe measurement errors, and two data-driven methods are proposed to segment regions of interests from RVAMs: 1) using group average RVAMs, and 2) using difference of group average RVAMs. Relative fixation times within regions of interest are calculated and used as input features for a vanilla multilayered perceptron to classify between patient groups. The methods were evaluated on data from an anorexia nervosa (AN) study with 37 subjects and a bipolar/major depressive disorder (BD-MDD) study with 73 subjects. Using leave-one-subject-out cross validation, our technique is able to achieve an area under the receiver operating curve (AUROC) score of 0.935 for the AN study and 0.888 for the BD-MDD study, the latter of which exceeds the performance of the state-of-the-art analysis model designed specifically for the BD-MDD study, which had an AUROC of 0.879. The results validate the proposed methods' efficacy as generalizable, standard baselines for analyzing visual scanning data.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

Abstract Poster Similar

Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

Holistic Grid Fusion Based Stop Line Estimation

Runsheng Xu, Faezeh Tafazzoli, Li Zhang, Timo Rehfeld, Gunther Krehl, Arunava Seal

Auto-TLDR; Fused Multi-Sensory Data for Stop Lines Detection in Intersection Scenarios

Abstract Slides Similar

Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufficient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

A Two-Step Approach to Lidar-Camera Calibration

Yingna Su, Yaqing Ding, Jian Yang, Hui Kong

Auto-TLDR; Closed-Form Calibration of Lidar-camera System for Ego-motion Estimation and Scene Understanding

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Autonomous vehicles and robots are typically equipped with Lidar and camera. Hence, calibrating the Lidar-camera system is of extreme importance for ego-motion estimation and scene understanding. In this paper, we propose a two-step approach (coarse + fine) for the external calibration between a camera and a multiple-line Lidar. First, a new closed-form solution is proposed to obtain the initial calibration parameters. We compare our solution with the state-of-the-art SVD-based algorithm, and show the benefits of both the efficiency and stability. With the initial calibration parameters, the ICP-based calibration framework is used to register the point clouds which extracted from the camera and Lidar coordinate frames, respectively. Our method has been applied to two Lidar-camera systems: an HDL-64E Lidar-camera system, and a VLP-16 Lidar-camera system. Experimental results demonstrate that our method achieves promising performance and higher accuracy than two open-source methods.

Extending Single Beam Lidar to Full Resolution by Fusing with Single Image Depth Estimation

Yawen Lu, Yuxing Wang, Devarth Parikh, Guoyu Lu

Auto-TLDR; Self-supervised LIDAR for Low-Cost Depth Estimation

Abstract Slides Similar

Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution Lidars are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a learning-based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 50-100 times cheaper price (e.g., \$80000 Velodyne HDL-64E LIDAR v.s. \$1000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

Inner Eye Canthus Localization for Human Body Temperature Screening

Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto Del Bimbo

Auto-TLDR; Automatic Localization of the Inner Eye Canthus in Thermal Face Images using 3D Morphable Face Model

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In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released.

Polarimetric Image Augmentation

Marc Blanchon, Fabrice Meriaudeau, Olivier Morel, Ralph Seulin, Desire Sidibe

Auto-TLDR; Polarimetric Augmentation for Deep Learning in Robotics Applications

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This paper deals with new augmentation methods for an unconventional imaging modality sensitive to the physics of the observed scene called polarimetry. In nature, polarized light is obtained by reﬂection or scattering. Robotics applications in urban environments are subject to many obstacles that can be specular and therefore provide polarized light. These areas are prone to segmentation errors using standard modalities but could be solved using information carried by the polarized light. Deep Convolutional Neural Networks (DCNNs) have shown excellent segmentation results, but require a signiﬁcant amount of data to achieve best performances. The lack of data is usually overcomed by using augmentation methods. However, unlike RGB images, polarization images are not only scalar (intensity) images and standard augmentation techniques cannot be applied straightforwardly. We propose enhancing deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions. We subsequently observe an average of 18.1% improvement in IoU between not augmented and regularized training procedures on real world data.

NetCalib: A Novel Approach for LiDAR-Camera Auto-Calibration Based on Deep Learning

Shan Wu, Amnir Hadachi, Damien Vivet, Yadu Prabhakar

Auto-TLDR; Automatic Calibration of LiDAR and Cameras using Deep Neural Network

Abstract Slides Poster Similar

A fusion of LiDAR and cameras have been widely used in many robotics applications such as classification, segmentation, object detection, and autonomous driving. It is essential that the LiDAR sensor can measure distances accurately, which is a good complement to the cameras. Hence, calibrating sensors before deployment is a mandatory step. The conventional methods include checkerboards, specific patterns, or human labeling, which is trivial and human-labor extensive if we do the same calibration process every time. The main propose of this research work is to build a deep neural network that is capable of automatically finding the geometric transformation between LiDAR and cameras. The results show that our model manages to find the transformations from randomly sampled artificial errors. Besides, our work is open-sourced for the community to fully utilize the advances of the methodology for developing more the approach, initiating collaboration, and innovation in the topic.

Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution

Renshu Gu, Gaoang Wang, Jenq-Neng Hwang

Auto-TLDR; 3D Human Pose Estimation for Multi-Human Videos with Occlusion

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3D human pose estimation (HPE) is crucial in human behavior analysis, augmented reality/virtual reality (AR/VR) applications, and self-driving industry. Videos that contain multiple potentially occluded people captured from freely moving monocular cameras are very common in real-world scenarios, while 3D HPE for such scenarios is quite challenging, partially because there is a lack of such data with accurate 3D ground truth labels in existing datasets. In this paper, we propose a temporal regression network with a gated convolution module to transform 2D joints to 3D and recover the missing occluded joints in the meantime. A simple yet effective localization approach is further conducted to transform the normalized pose to the global trajectory. To verify the effectiveness of our approach, we also collect a new moving camera multi-human (MMHuman) dataset that includes multiple people with heavy occlusion captured by moving cameras. The 3D ground truth joints are provided by accurate motion capture (MoCap) system. From the experiments on static-camera based Human3.6M data and our own collected moving-camera based data, we show that our proposed method outperforms most state-of-the-art 2D-to-3D pose estimation methods, especially for the scenarios with heavy occlusions.

Camera Calibration Using Parallel Line Segments

Gaku Nakano

Auto-TLDR; Closed-Form Calibration of Surveillance Cameras using Parallel 3D Line Segment Projections

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This paper proposes a camera calibration method suitable for surveillance cameras using the image projection of parallel 3D line segments of the same length. We assume that vertical line segments are perpendicular to the ground plane and their bottom end-points are on the ground plane. Under this assumption, the camera parameters can be directly solved by at least two line segments without estimating vanishing points. Extending the minimal solution, we derive a closed-form solution to the least squares case with more than two line segments. Lens distortion is jointly optimized in bundle adjustment. Synthetic data evaluation shows that the best depression angle of a camera is around 50 degrees. In real data evaluation, we use body joints of pedestrians as vertical line segments. The experimental results on publicly available datasets show that the proposed method with a human pose detector can correctly calibrate wide-angle cameras including radial distortion.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané

Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

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Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Mobile Augmented Reality: Fast, Precise, and Smooth Planar Object Tracking

Dmitrii Matveichev, Daw-Tung Lin

Auto-TLDR; Planar Object Tracking with Sparse Optical Flow Tracking and Descriptor Matching

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We propose an innovative method for combining sparse optical flow tracking and descriptor matching algorithms. The proposed approach solves the following problems that are inherent to keypoint-based and optical flow based tracking algorithms: spatial jitter, extreme scale transformation, extreme perspective transformation, degradation in the number of tracking points, and drifting of tracking points. Our algorithm provides smooth object-position tracking under six degrees of freedom transformations with a small computational cost for providing a high-quality real-time AR experience on mobile platforms. We experimentally demonstrate that our approach outperforms the state-of-the-art tracking algorithms while offering faster computational time. A mobile augmented reality (AR) application, which is developed using our approach, delivers planar object tracking with 30 FPS on modern mobile phones for a camera resolution of 1280$\times$720. Finally, we compare the performance of our AR application with that of the Vuforia-based AR application on the same planar objects database. The test results show that our AR application delivers better AR experience than Vuforia in terms of smooth transition of object-pose between video frames.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

Wireless Localisation in WiFi Using Novel Deep Architectures

Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

Auto-TLDR; Deep Neural Network for Indoor Localisation of WiFi Devices in Indoor Environments

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This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.

Approach for Document Detection by Contours and Contrasts

Daniil Tropin, Sergey Ilyuhin, Dmitry Nikolaev, Vladimir V. Arlazarov

Auto-TLDR; A countor-based method for arbitrary document detection on a mobile device

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This paper considers the task of arbitrary document detection performed on a mobile device. The classical contour-based approach often mishandles cases with occlusion, complex background, or blur. Region-based approach, which relies on the contrast between object and background, does not have limitations, however its known implementations are highly resource-consuming. We propose a modification of a countor-based method, in which the competing hypotheses of the contour location are ranked according to the contrast between the areas inside and outside the border. In the performed experiments such modification leads to the 40% decrease of alternatives ordering errors and 10% decrease of the overall number of detection errors. We updated state-of-the-art performance on the open MIDV-500 dataset and demonstrated competitive results with the state-of-the-art on the SmartDoc dataset.

5D Light Field Synthesis from a Monocular Video

Kyuho Bae, Andre Ivan, Hajime Nagahara, In Kyu Park

Auto-TLDR; Synthesis of Light Field Video from Monocular Video using Deep Learning

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Commercially available light field cameras have difficulty in capturing 5D (4D + time) light field videos. They can only capture still light filed images or are excessively expensive for normal users to capture the light field video. To tackle this problem, we propose a deep learning-based method for synthesizing a light field video from a monocular video. We propose a new synthetic light field video dataset that renders photorealistic scenes using Unreal Engine because no light field video dataset is available. The proposed deep learning framework synthesizes the light field video with a full set (9x9) of sub-aperture images from a normal monocular video. The proposed network consists of three sub-networks, namely, feature extraction, 5D light field video synthesis, and temporal consistency refinement. Experimental results show that our model can successfully synthesize the light field video for synthetic and real scenes and outperforms the previous frame-by-frame method quantitatively and qualitatively.

RefiNet: 3D Human Pose Refinement with Depth Maps

Andrea D'Eusanio, Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara

Auto-TLDR; RefiNet: A Multi-stage Framework for 3D Human Pose Estimation

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Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manually-annotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map. The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds. Moreover, we collect a new dataset, namely Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context. Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.

OmniFlowNet: A Perspective Neural Network Adaptation for Optical Flow Estimation in Omnidirectional Images

Charles-Olivier Artizzu, Haozhou Zhang, Guillaume Allibert, Cédric Demonceaux

Auto-TLDR; OmniFlowNet: A Convolutional Neural Network for Omnidirectional Optical Flow Estimation

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Spherical cameras and the latest image processing techniques open up new horizons. In particular, methods based on Convolutional Neural Networks (CNNs) now give excellent results for optical flow estimation on perspective images. However, these approaches are highly dependent on their architectures and training datasets. This paper proposes to benefit from years of improvement in perspective images optical flow estimation and to apply it to omnidirectional ones without training on new datasets. Our network, OmniFlowNet, is built on a CNN specialized in perspective images. Its convolution operation is adapted to be consistent with the equirectangular projection. Tested on spherical datasets created with Blender and several equirectangular videos realized from real indoor and outdoor scenes, OmniFlowNet shows better performance than its original network.

Collaborative Human Machine Attention Module for Character Recognition

Chetan Ralekar, Tapan Gandhi, Santanu Chaudhury

Auto-TLDR; A Collaborative Human-Machine Attention Module for Deep Neural Networks

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The deep learning models which include attention mechanisms are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider `attention' as a pure machine vision optimization problem and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network's attention. The proposed module is inspired by the dorsal (`where') pathways of visual processing and it can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminative image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in the finer refinement in feature maps which results in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other vision tasks as well.

P2D: A Self-Supervised Method for Depth Estimation from Polarimetry

Marc Blanchon, Desire Sidibe, Olivier Morel, Ralph Seulin, Daniel Braun, Fabrice Meriaudeau

Auto-TLDR; Polarimetric Regularization for Monocular Depth Estimation

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Monocular depth estimation is a recurring subject in the ﬁeld of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we include a term of polarimetric regularization to state-of-the-art method to take speciﬁc advantage of the data. Our method is evaluated both qualitatively and quantitatively demonstrating that the contribution of this new information as well as an enhanced loss function improves depth estimation results, especially for specular areas.

DE-Net: Dilated Encoder Network for Automated Tongue Segmentation

Hui Tang, Bin Wang, Jun Zhou, Yongsheng Gao

Auto-TLDR; Automated Tongue Image Segmentation using De-Net

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Automated tongue recognition is a growing research field due to global demand for personal health care. Using mobile devices to take tongue pictures is convenient and of low cost for tongue recognition. It is particularly suitable for self-health evaluation of the public. However, images taken by mobile devices are easily affected by various imaging environment, which makes fine segmentation a more challenging task compared with those taken by specialized acquisition devices. Deep learning approaches are promising for tongue image segmentation because they have powerful feature learning and representation capability. However, the successive pooling operations in these methods lead to loss of information on image details, making them fail when segmenting low-quality images captured by mobile devices. To address this issue, we propose a dilated encoder network (DE-Net) to capture more high-level features and get high-resolution output for automated tongue image segmentation. In addition, we construct two tongue image datasets which contain images taken by specialized devices and mobile devices, respectively, to verify the effectiveness of the proposed method. Experimental results on both datasets demonstrate that the proposed method outperforms the state-of-the-art methods in tongue image segmentation.

Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video

Pu Li, Xiaobai Liu

Auto-TLDR; Sequential Estimation of Planar Homographic Transformations over Aerial Videos

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This paper presents an unsupervised approach that leverages raw aerial videos to learn to estimate planar homographic transformation between consecutive video frames. Previous learning-based estimators work on pairs of images to estimate their planar homographic transformations but suffer from severe over-fitting issues, especially when applying over aerial videos. To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches. We also incorporate a set of spatial-temporal knowledge to regularize the learning of such a sequence-to-sequence model. We collect a set of challenging aerial videos and compare the proposed method to the alternative algorithms. Empirical studies suggest that our sequential model achieves significant improvement over alternative image-based methods and the knowledge-rich regularization further boosts our system performance. Our codes and dataset could be found at https://github.com/Paul-LiPu/DeepVideoHomography

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

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In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen

Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

GazeMAE: General Representations of Eye Movements Using a Micro-Macro Autoencoder

Louise Gillian C. Bautista, Prospero Naval

Auto-TLDR; Fast and Slow Eye Movement Representations for Sentiment-agnostic Eye Tracking

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Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position and velocity signals and train a deep temporal convolutional autoencoder to learn micro-scale and macro-scale representations corresponding to the fast and slow features of eye movements. These joint representations are evaluated by fitting a linear classifier on various tasks and outperform other works in biometrics and stimuli classification. Further experiments highlight the validity and generalizability of this method, bringing eye tracking research closer to real-world applications.

Rotational Adjoint Methods for Learning-Free 3D Human Pose Estimation from IMU Data

Caterina Emilia Agelide Buizza, Yiannis Demiris

Auto-TLDR; Learning-free 3D Human Pose Estimation from Inertial Measurement Unit Data

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We present a new framework for learning-free 3D human pose estimation from Inertial Measurement Unit (IMU) data. The proposed method does not rely on a full motion sequence to calculate a pose for any particular time point and thus can operate in real-time. A cost function based only on joint rotations is used, removing the need for frequent transformations between rotations and 3D Cartesian coordinates. A Jacobian that preserves skeleton structure is derived using Adjoint methods from Variational Data Assimilation. To facilitate further research in IMU-based Motion Capture, we provide a dataset that combines RGB and depth images from an Intel RealSense camera, marker-based motion capture from an Optitrack system and Xsens IMU data. We have evaluated our method on both our dataset and the Total Capture dataset, showing an average error across 24 joints of 0.45 and 0.48 radians respectively.

3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning

Mengqi Rong, Shuhan Shen, Zhanyi Hu

Auto-TLDR; 3D Semantic Expression of Urban Scenes Based on Active Learning

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As different urban scenes are similar but still not completely consistent, coupled with the complexity of labeling directly in 3D, high-level understanding of 3D scenes has always been a tricky problem. In this paper, we propose a procedural approach for 3D semantic expression of urban scenes based on active learning. We first start with a small labeled image set to fine-tune a semantic segmentation network and then project its probability map onto a 3D mesh model for fusion, finally outputs a 3D semantic mesh model in which each facet has a semantic label and a heat model showing each facet’s confidence. Our key observation is that our algorithm is iterative, in each iteration, we use the output semantic model as a supervision to select several valuable images for annotation to co-participate in the fine-tuning for overall improvement. In this way, we reduce the workload of labeling but not the quality of 3D semantic model. Using urban areas from two different cities, we show the potential of our method and demonstrate its effectiveness.

PolyLaneNet: Lane Estimation Via Deep Polynomial Regression

Talles Torres, Rodrigo Berriel, Thiago Paixão, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos

Auto-TLDR; Real-Time Lane Detection with Deep Polynomial Regression

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One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to be solved completely is lane detection. Since methods for this task have to work in real time (+30 FPS), they not only have to be effective (i.e., have high accuracy) but they also have to be efficient (i.e., fast). In this work, we present a novel method for lane detection that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression. The proposed method is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset, while maintaining its efficiency (115 FPS). Additionally, extensive qualitative results on two additional public datasets are presented, alongside with limitations in the evaluation metrics used by recent works for lane detection. Finally, we provide source code and trained models that allow others to replicate all the results shown in this paper, which is surprisingly rare in state-of-the-art lane detection methods.

Edge-Aware Monocular Dense Depth Estimation with Morphology

Zhi Li, Xiaoyang Zhu, Haitao Yu, Qi Zhang, Yongshi Jiang

Auto-TLDR; Spatio-Temporally Smooth Dense Depth Maps Using Only a CPU

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Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, a Warm-Start initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

Benchmarking Cameras for OpenVSLAM Indoors

Kevin Chappellet, Guillaume Caron, Fumio Kanehiro, Ken Sakurada, Abderrahmane Kheddar

Auto-TLDR; OpenVSLAM: Benchmarking Camera Types for Visual Simultaneous Localization and Mapping

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In this paper we benchmark different types of cameras and evaluate their performance in terms of reliable localization reliability and precision in Visual Simultaneous Localization and Mapping (vSLAM). Such benchmarking is merely found for visual odometry, but never for vSLAM. Existing studies usually compare several algorithms for a given camera. %This work is the first to handle the dual of the latter, i.e. comparing several cameras for a given SLAM algorithm. The evaluation methodology we propose is applied to the recent OpenVSLAM framework. The latter is versatile enough to natively deal with perspective, fisheye, 360 cameras in a monocular or stereoscopic setup, an in RGB or RGB-D modalities. Results in various sequences containing light variation and scenery modifications in the scene assess quantitatively the maximum localization rate for 360 vision. In the contrary, RGB-D vision shows the lowest localization rate, but highest precision when localization is possible. Stereo-fisheye trades-off with localization rates and precision between 360 vision and RGB-D vision. The dataset with ground truth will be made available in open access to allow evaluating other/future vSLAM algorithms with respect to these camera types.

A Lumen Segmentation Method in Ureteroscopy Images Based on a Deep Residual U-Net Architecture

Jorge Lazo, Marzullo Aldo, Sara Moccia, Michele Catellani, Benoit Rosa, Elena De Momi, Michel De Mathelin, Francesco Calimeri

Auto-TLDR; A Deep Neural Network for Ureteroscopy with Residual Units

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Ureteroscopy is becoming the first surgical treatment option for the majority of urinary affections. This procedure is carried out using an endoscope which provides the surgeon with the visual and spatial information necessary to navigate inside the urinary tract. Having in mind the development of surgical assistance systems, that could enhance the performance of surgeon, the task of lumen segmentation is a fundamental part since this is the visual reference which marks the path that the endoscope should follow. This is something that has not been analyzed in ureteroscopy data before. However, this task presents several challenges given the image quality and the conditions itself of ureteroscopy procedures. In this paper, we study the implementation of a Deep Neural Network which exploits the advantage of residual units in an architecture based on U-Net. For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images. We found that training on gray-scale images gives the best results obtaining mean values of Dice Score, Precision, and Recall of 0.73, 0.58, and 0.92 respectively. The results obtained show that the use of residual U-Net could be a suitable model for further development for a computer-aided system for navigation and guidance through the urinary system.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou

Auto-TLDR; Food Volume Estimation from a Single Food Image via Geometric Understanding and Semantic Prediction

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Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.

Real-Time Driver Drowsiness Detection Using Facial Action Units

Malaika Vijay, Nandagopal Netrakanti Vinayak, Maanvi Nunna, Subramanyam Natarajan

Auto-TLDR; Real-Time Detection of Driver Drowsiness using Facial Action Units using Extreme Gradient Boosting

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This paper presents a two-stage, vision-based pipeline for the real-time detection of driver drowsiness using Facial Action Units (FAUs). FAUs capture movements in groups of muscles in the face like widening of the eyes or dropping of the jaw. The first stage of the pipeline employs a Convolutional Neural Network (CNN) trained to detect FAUs. The output of the penultimate layer of this network serves as an image embedding that captures features relevant to FAU detection. These embeddings are then used to predict drowsiness using an Extreme Gradient Boosting (XGBoost) classifier. A separate XGBoost model is trained for each user of the system so that behavior specific to each user can be modeled into the drowsiness classifier. We show that user-specific classifiers require very little data and low training time to yield high prediction accuracies in real-time.

Siamese Fully Convolutional Tracker with Motion Correction

Mathew Francis, Prithwijit Guha

Auto-TLDR; A Siamese Ensemble for Visual Tracking with Appearance and Motion Components

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Visual tracking algorithms use cues like appearance, structure, motion etc. for locating an object in a video. We propose an ensemble tracker with appearance and motion components. A siamese tracker that learns object appearance from a static image and motion vectors computed between consecutive frames with a flow network forms the ensemble. Motion predicted object localization is used to correct the appearance component in the ensemble. Complementary nature of the components bring performance improvement as observed in experiments performed on VOT2018 and VOT2019 datasets.

Light3DPose: Real-Time Multi-Person 3D Pose Estimation from Multiple Views

Alessio Elmi, Davide Mazzini, Pietro Tortella

Auto-TLDR; 3D Pose Estimation of Multiple People from a Few calibrated Camera Views using Deep Learning

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We present an approach to perform 3D pose estimation of multiple people from a few calibrated camera views. Our architecture, leveraging the recently proposed unprojection layer, aggregates feature-maps from a 2D pose estimator backbone into a comprehensive representation of the 3D scene. Such intermediate representation is then elaborated by a fully-convolutional volumetric network and a decoding stage to extract 3D skeletons with sub-voxel accuracy. Our method achieves state of the art MPJPE on the CMU Panoptic dataset using a few unseen views and obtains competitive results even with a single input view. We also assess the transfer learning capabilities of the model by testing it against the publicly available Shelf dataset obtaining good performance metrics. The proposed method is inherently efficient: as a pure bottom-up approach, it is computationally independent of the number of people in the scene. Furthermore, even though the computational burden of the 2D part scales linearly with the number of input views, the overall architecture is able to exploit a very lightweight 2D backbone which is orders of magnitude faster than the volumetric counterpart, resulting in fast inference time. The system can run at 6 FPS, processing up to 10 camera views on a single 1080Ti GPU.

One Step Clustering Based on A-Contrario Framework for Detection of Alterations in Historical Violins

Alireza Rezaei, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi

Auto-TLDR; A-Contrario Clustering for the Detection of Altered Violins using UVIFL Images

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Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of interventions necessary. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the ``Violins UVIFL imagery'' dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with the state of the art clustering methods shows improved overall precision and recall.

Removing Raindrops from a Single Image Using Synthetic Data

Yoshihito Kokubo, Shusaku Asada, Hirotaka Maruyama, Masaru Koide, Kohei Yamamoto, Yoshihisa Suetsugu

Auto-TLDR; Raindrop Removal Using Synthetic Raindrop Data

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We simulated the exact features of raindrops on a camera lens and conducted an experiment to evaluate the performance of a network trained to remove raindrops using synthetic raindrop data. Although research has been conducted to precisely evaluate methods to remove raindrops, with some evaluation networks trained on images with real raindrops and others trained on images with synthetic raindrops, there have not been any studies that have directly compared the performance of two networks trained on each respective kind of image. In a previous study wherein images with synthetic raindrops were used for training, the network did not work effectively on images with real raindrops because the shapes of the raindrops were simulated using simple arithmetic expressions. In this study, we focused on generating raindrop shapes that are closer to reality with the aim of using these synthetic raindrops in images to develop a technique for removing real-world raindrops. After categorizing raindrops by type, we further separated each raindrop type into its constituent elements, generated each element separately, and finally combined the generated elements. The proposed technique was used to add images with synthetic raindrops to the training data, and when we evaluated the model, we confirmed that the technique's precision exceeded that of when only images with actual raindrops were used for training. The evaluation results proved that images with synthetic raindrops can be used as training data for real-world images.