The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target re- detection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.

Anthropometric body measurements are important for industrial design, garment fitting, medical diagnosis and ergonomics. A number of methods have been proposed to estimate the body measurements from images, but progress has been slow due to the lack of realistic and publicly available datasets. The existing works train and test on silhouettes of 3D body meshes obtained by fitting a human body model to the commercial CAESAR scans. In this work, we introduce the BODY-fit dataset that contains fitted meshes of 2,675 female and 1,474 male 3D body scans. We unify evaluation on the CAESAR-fit and BODY-fit datasets by computing body measurements from geodesic surface paths as the ground truth and by generating two-view silhouette images. We also introduce BODY-rgb - a realistic dataset of 86 male and 108 female subjects captured with an RGB camera and manually tape measured ground truth. We propose a simple yet effective deep CNN architecture as a baseline method which obtains competitive accuracy on the three datasets.