Articulated body motion capture by annealed particle filtering

The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints: either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, we aim for general tracking without special preparation of objects or restrictive assumptions. The principal contribution of the paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle based on annealing to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently.

[1]  David C. Hogg.  Model-based vision: a program to see a walking person , 1983, Image Vis. Comput..

[2]  J. Geweke,et al.  Bayesian Inference in Econometric Models Using Monte Carlo Integration , 1989 .

[3]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Pietro Perona,et al.  Monocular tracking of the human arm in 3D , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  Michael J. Black,et al.  Cardboard people: a parameterized model of articulated image motion , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[6]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[7]  Pietro Perona,et al.  Monocular tracking of the human arm in 3D: real-time implementation and experiments , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[8]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Michael J. Black,et al.  Cardboard people: A parametrized model of articulated motion , 1996 .

[10]  Karl Rohr,et al.  Human Movement Analysis Based on Explicit Motion Models , 1997 .

[11]  Mubarak Shah,et al.  Motion-Based Recognition , 1997, Computational Imaging and Vision.

[12]  Larry S. Davis,et al.  W4S : A real-time system for detecting and tracking people in 2 D , 1998, eccv 1998.

[13]  L. Davis,et al.  W 4 S: a Real-time System for Detecting and Tracking People in 2 1 2 D , 1998 .

[14]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[15]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[16]  Andrew Blake,et al.  Tracking through singularities and discontinuities by random sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  Michael Isard,et al.  Object localization by Bayesian correlation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[19]  James M. Rehg,et al.  Singularities in Articulated Object Tracking with 2-D and 3-D Models , 2001 .