3D tracking for gait characterization and recognition

We propose an approach to gait analysis that relies on fitting 3D temporal motion models to synchronized video sequences. These models allow us not only to track but also to recover motion parameters that can be used to recognize people and characterize their style. Because our method is robust to occlusions and insensitive to changes in direction of motion, our proposed approach has the potential to overcome some of the main limitations of current gait analysis methods. This is an important step towards taking biometrics out of the laboratory and into the real world.

[1]  Yali Amit,et al.  Joint Induction of Shape Features and Tree Classifiers , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[3]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[4]  Hiroshi Murase,et al.  Moving object recognition in eigenspace representation: gait analysis and lip reading , 1996, Pattern Recognit. Lett..

[5]  David J. Fleet,et al.  People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  ChewYean Yam,et al.  On the relationship of human walking and running: automatic person identification by gait , 2002, Object recognition supported by user interaction for service robots.

[7]  Mark S. Nixon,et al.  Comparing Different Template Features for Recognizing People by their Gait , 1998, BMVC.

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Peter L. Bartlett,et al.  Boosting Algorithms as Gradient Descent , 1999, NIPS.

[11]  Chiraz Ben Abdelkader.  Motion-Based Recognition of People in EigenGait Space , 2002 .

[12]  Ian D. Reid,et al.  Articulated Body Motion Capture by Stochastic Search , 2005, International Journal of Computer Vision.

[13]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[14]  Cristian Sminchisescu,et al.  Kinematic jump processes for monocular 3D human tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  Qiang He,et al.  Individual recognition from periodic activity using hidden Markov models , 2000, Proceedings Workshop on Human Motion.

[16]  Andrew Blake,et al.  Markerless motion capture of complex full-body movement for character anima-tion , 2001, CVPR 2000.

[17]  Heinrich Niemann,et al.  Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities , 1998, BMVC.

[18]  Michael J. Black,et al.  Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.

[19]  J. Little,et al.  Recognizing People by Their Gait: The Shape of Motion , 1998 .

[20]  Pascal Fua,et al.  3D Human Body Tracking Using Deterministic Temporal Motion Models , 2004, ECCV.

[21]  Pascal Fua,et al.  Articulated Soft Objects for Multiview Shape and Motion Capture , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Mark S. Nixon,et al.  Automatic extraction and description of human gait models for recognition purposes , 2003, Comput. Vis. Image Underst..

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