Robust tracking and segmentation of human motion in an image sequence

We present a method for improving robustness in feature-based tracking of human motion. Motion flows of features estimated by a standard tracker are modified to be coherent with neighboring ones. This coherence constraint is computed based on a smooth approximation to initial motion flows computed by the tracker. With these tracking results, we demonstrate motion segmentation of different body parts in an image sequence.

[1]  R. Plankers,et al.  Articulated soft objects for video-based body modeling , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

[3]  Andrew Zisserman,et al.  3D Motion recovery via affine Epipolar geometry , 1995, International Journal of Computer Vision.

[4]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[6]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Cristian Sminchisescu,et al.  Covariance scaled sampling for monocular 3D body tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  Olivier D. Faugeras,et al.  3D articulated models and multi-view tracking with silhouettes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Pascal Fua,et al.  Articulated Soft Objects for Video-based Body Modeling , 2001, ICCV.

[11]  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.

[12]  Ioannis A. Kakadiaris,et al.  3D human body model acquisition from multiple views , 1995, Proceedings of IEEE International Conference on Computer Vision.

[13]  Roberto Cipolla,et al.  Real-time tracking of highly articulated structures in the presence of noisy measurements , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[15]  N. Alberto Borghese,et al.  Hierarchical RBF networks and local parameters estimate , 1998, Neurocomputing.