Multi-people pose tracking through voxel streams

Vision based human articulated body pose tracking has been historically important. Because analyzing multiple human activities, especially interaction between human in cluttered scenes is essential in visual surveillance scenarios, multiple people tracking has enjoyed much attention in human robot interaction research in recent years. In this paper, we newly introduce a robust framework for multiple people pose tracking. The notable aspects of our approach are real-time ensuring speed (up to 30 fps), flexibility towards various complex motions and environments. Our work is inspired by the success of multiple view approach, especially voxel based techniques. The use of voxel data leads to viewpoint-free estimation, which benefits in that reconstruction of a training model is needless in different multi-camera arrangements. We add simple tracking-based volume segmentation algorithm to retain practical superiority of voxel based approach. Furthermore, our framework successfully obtains multiple body pose estimation in real-time even when people contacts with each other occurs in the scene, which is not addressed in the conventional approaches. We demonstrate the effectiveness of our approach with experiments on indoor cluttered scene sequences.