An efficient stochastic framework for 3D human motion tracking

Bingbing Ni; Stefan Winkler; Ashraf Ali Kassim

doi:10.1117/12.765767

25 February 2008 An efficient stochastic framework for 3D human motion tracking

Bingbing Ni, Stefan Winkler, Ashraf Ali Kassim

Proceedings Volume 6805, Three-Dimensional Image Capture and Applications 2008; 68050R (2008) https://doi.org/10.1117/12.765767
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

In this paper, we present a stochastic framework for articulated 3D human motion tracking. Tracking full body human motion is a challenging task, because the tracking performance normally suffers from several issues such as self-occlusion, foreground segmentation noise and high computational cost. In our work, we use explicit 3D reconstructions of the human body based on a visual hull algorithm as our system input, which effectively eliminates self-occlusion. To improve tracking efficiency as well as robustness, we use a Kalman particle filter framework based on an interacting multiple model (IMM). The posterior density is approximated by a set of weighted particles, which include both sample means and covariances. Therefore, tracking is equivalent to searching the maximum a posteriori (MAP) of the probability distribution. During Kalman filtering, several dynamical models of human motion (e.g., zero order, first order) are assumed which interact with each other for more robust tracking results. Our measurement step is performed by a local optimization method using simulated physical force/moment for 3D registration. The likelihood function is designed to be the fitting score between the reconstructed human body and our 3D human model, which is composed of a set of cylinders. This proposed tracking framework is tested on a real motion sequence. Our experimental results show that the proposed method improves the sampling efficiency compared with most particle filter based methods and achieves high tracking accuracy.

Citation Download Citation

Bingbing Ni, Stefan Winkler, and Ashraf Ali Kassim "An efficient stochastic framework for 3D human motion tracking", Proc. SPIE 6805, Three-Dimensional Image Capture and Applications 2008, 68050R (25 February 2008); https://doi.org/10.1117/12.765767

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