Improving human body part detection using deep learning and motion consistency

Body part segmentation and detection in videos is a useful analysis for many computer vision tasks such as action recognition and video search. Conventional methods mainly focus on body part detection assuming upright posture of the human body. Recently, a body part detection framework was proposed to include non-upright postures. This method consists of 2 parts, initial segmentation and computation of body part likelihood score for each segment. In this paper, we propose improvements to this approach. Firstly, we propose a novel motion based body part segmentation using kinematic features to identify segments which undergo similar motion in the video based on a consistency or error measure. Secondly, we replace the Extreme Learning Machine classifier in the original work with deep learning to investigate it's performance. For accurate detection, deep learning requires a lot of training data and it has so far been used only in high resolution images. Here we apply deep learning for body part detection in low resolution cases. We conduct experiments to study and analyse the effect of the improvements proposed.