Tilting at windmills
Data augmentation for deep pose estimation does not help with occlusions
R.J. Pytel (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jan van Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
O.S. Kayhan – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Marcel Reinders – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Cynthia CS Liem – Graduation committee member (TU Delft - Multimedia Computing)
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Abstract
Occlusion degrades the performance of human pose estimation. In this paper, we introduce targeted keypoint and body part occlusion attacks. The effects of the attacks are systematically analyzed on the best-performing methods. In addition, we propose occlusion specific data augmentation techniques against keypoint and part attacks. Our extensive experiments show that human pose estimation methods are not robust to occlusion and data augmentation does not solve the occlusion problems.