Robust Human Pose Estimation for Rotation via Self-Supervised Learning

The detection of abnormal postures, such as that of a reclining person, is a crucial part of visual surveillance. Further, even regular poses can appear rotated because of incongruity between the image and the angle of a pre-installed camera. However, most existing human pose estimation methods focus on small rotational changes, i.e., those less than 50 degrees, and they seldom consider robust human pose estimation for more drastic rotational changes. To the best of our knowledge, there have been no reports on the robustness of human pose estimation for rotational changes through large angles. In this study, we propose a robust human pose estimation method by creating a path for learning new rotational changes based on a self-supervised method and by combining the results with those obtained from a path based on a supervised method. Furthermore, a combination module composed of a convolutional layer is trained complementarily by both paths of the network to produce robust results for various rotational changes. We demonstrate the robustness of the proposed method with extensive experiments on images generated by rotating the elements of standard benchmark datasets. We fully analyze the rotational characteristics of the state-of-the-art human pose estimators and the proposed method. On the COCO Keypoint Detection dataset, the proposed method attains more than 15% improvement in the mean of average precision compared to the state-of-the-art method, and the standard deviation of the performance is improved by more than 4.7 times.