Bayesian human segmentation in crowded situations

The problem of segmenting individual humans in crowded situations from stationary video camera sequences is exacerbated by object inter-occlusion. We pose this problem as a "model-based segmentation" problem in which human shape models are used to interpret the foreground in a Bayesian framework. The solution is obtained by using an efficient Markov chain Monte Carlo (MCMC) method that uses domain knowledge as proposal probabilities. Knowledge of various aspects including human shape, human height, camera model, and image cues including human head candidates, foreground/background separation are integrated in one theoretically sound framework. We show promising results and evaluations on some challenging data.

[1]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Sylvia Richardson,et al.  Markov chain concepts related to sampling algorithms , 1995 .

[4]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Hai Tao,et al.  A Sampling Algorithm for Tracking Multiple Objects , 1999, Workshop on Vision Algorithms.

[6]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Rong Zhang,et al.  Integrating bottom-up/top-down for object recognition by data driven Markov chain Monte Carlo , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Larry S. Davis,et al.  Probabilistic framework for segmenting people under occlusion , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Harry Shum,et al.  Image segmentation by data driven Markov chain Monte Carlo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Ramakant Nevatia,et al.  Segmentation and tracking of multiple humans in complex situations , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Zhuowen Tu,et al.  A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction , 2002, ECCV.

[15]  Stephen J. Maybank,et al.  Fusion of Multiple Tracking Algorithms for Robust People Tracking , 2002, ECCV.

[16]  Larry S. Davis,et al.  M2Tracker: A Multi-view Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo , 2002, ECCV.

[17]  Ramakant Nevatia,et al.  Stochastic human segmentation from a static camera , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..