  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.
