Randomized decision bush: Combining global shape parameters and local scalable descriptors for human body parts recognition

This paper presents a novel method which combines global shape parameters and scalable local descriptors for accurate body parts recognition from a single depth image in real-time. Human poses are of extremely large variation in aspects of visual shapes, because human can take poses from daily activities to gymnastic actions. In order to cover wide-range of the human poses, the proposed algorithm employs a unified structure which combines pose clustering and body parts classification. We name the proposed method Randomized Decision Bush (RDB). Specifically, global shape parameters which can discriminate coarse level shapes are utilized for pose clustering while scalable local shape descriptors are employed for accurate classification. RDB splits the various human poses into multiple clusters which contain similar shapes of the poses. As a result, it provides robust clustering which enables fine level classification within the cluster. The experimental results show improvements on recognizing body parts due to the pose clustering and classification with scalable local descriptors. Additionally, we significantly reduce the complexity of training a large number of human shapes.