Robust human pose estimation from distorted wide-angle images through iterative search of transformation parameters

Tracking human motion from video sequences is a well-known video surveillance technique, and many commercially available motion capture devices can now recognize human poses using a depth camera. However, depth camera systems are complicated and have limited optical fields of view. To overcome this problem, it is necessary to develop techniques for recognizing human motion in wide-angle images. In this study, we devised a method for tracking human motion that is robust to wide-angle image distortion. To do so, we developed a new multilayered convolutional neural network architecture for estimating the locations of human body parts in images along with associated transformation parameters that can be applied to a distorted wide-angle image on a frame-by-frame basis. The proposed method was applied to distorted wide-angle images, and its robustness was demonstrated via a quantitative evaluation of human joint prediction and a comparative analysis with a commercially available depth camera-based motion capture system.