Jiayu He

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Feature Pyramid Hierarchies for Multi-Scale Temporal Action Detection

Jiayu He, Guohui Li, Jun Lei
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in session PS T1.10

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Auto-TLDR; Temporal Action Detection using Pyramid Hierarchies and Multi-scale Feature Maps

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Temporal action detection is a challenging but promising task in video content analysis. It is in great demand in the field of public safety. The main difficulty of the task is precisely localizing activities in the video especially those short duration activities. And most of the existing methods can not achieve a satisfactory detection result. Our method addresses a key point to improve detection accuracy, which is to use multi-scale feature maps for regression and classification. In this paper, we introduce a novel network based on classification following proposal framework. In our network, a 3D feature pyramid hierarchies is built to enhance the ability of detecting short duration activities. The input RGB/Flow frames are first encoded by a 3D feature pyramid hierarchies, and this subnet produces multi-level feature maps. Then temporal proposal subnet uses these features to pick out proposals which might contain activity segments. Finally a pyramid region of interest (RoI) pooling pipeline and two fully connected layers reuse muti-level feature maps to refine the temporal boundaries of proposals and classify them. We use late feature fusion scheme to combine RGB and Flow information. The network is trained end-to-end and we evaluate it in THUMOS'14 dataset. Our network achieves a good result among typical methods. A further ablation test demonstrate that pyramid hierarchies is effective to improve detecting short duration activity segments.