Omni-directional multiperson tracking in meeting scenarios combining simulated annealing and particle filtering

This proposal deals with the topic of tracking an unknown number of persons with a monocular camera in indoor environments. Within this context the main tracking system requirements are defined not only by a robust determination of all human trajectories but also by a reliable recovery of all object identities especially for challenging situations like heavy occlusion or the reentry of a person. Regarding all these needs a novel approach has been developed combining a probabilistic particle filter framework with an heuristic simulated annealing technique ported to the tracking domain. While the inter frame correspondence of objects, i.e. the assignment of identities, is handled by the simulated annealing approach, the particle filter architecture will be responsible for both the classification of an object to be a person as well as a stable tracking of the respective trajectory. An active shape model is utilized to create weights for the particles and thus serves as an object classifier. Our system has been evaluated on several video sequences showing meeting scenarios with a different number of participants. Quantitative numbers based on a tracking evaluation scheme show, that our system is capable of not only accurately determining the number of persons visible in each scene but also of precisely tracking each human and correctly assigning a label.

[1]  G. Rigoll,et al.  Robust Omni-directional Multi-cue Tracking for Multiple Person Meeting Scenarious , 2006 .

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[4]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[7]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Cor J. Veenman,et al.  Resolving Motion Correspondence for Densely Moving Points , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Shai Avidan,et al.  Support Vector Tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Gérard G. Medioni,et al.  Object reacquisition using invariant appearance model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[11]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[12]  Jean-Marc Odobez,et al.  Evaluating Multi-Object Tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[13]  Charles Kervrann,et al.  Multiple-target tracking of 3D fluorescent objects based on simulated annealing , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[14]  Adam Baumberg,et al.  Learning deformable models for tracking human motion , 1996 .

[15]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).