Tracking body parts of multiple people: a new approach

Abstract

Tracking body parts of multiple people in a video sequence is very useful for face/gesture recognition systems as well as human computer interaction (HCI) interfaces. This paper describes a framework for tracking multiple objects (e.g., hands and faces of multiple people) in a video stream. We use a probabilistic model to fuse the color and motion information to localize the body parts and employ a multiple hypothesis tracking (MHT) algorithm to track these features simultaneously. The MHT algorithm is capable of tracking multiple objects with limited occlusions and is suitable for resolving any data association uncertainty. We incorporated a path coherence function along with MHT to reduce the negative effects of spurious measurements that produce unconvincing tracks and needless computations. The performance of the framework has been validated using experiments on synthetic and real sequence of images.