Multiple Persons Tracking with Data Fusion of Multiple Cameras and Floor Sensors Using Particle Filters

Successful multi-target tracking requires locating the targets and labeling their identities. For the multi-target tracking systems, the latter becomes more challenging when the targets frequently interact with each other. In this paper, we propose a method for multiple persons tracking using multiple cameras and floor sensors. Our method estimates 3D positions of human body and head, and labels their identities. The method is composed of multiple particle filters that interact only in the exclusion occlusion model. Each particle filter tracks each person correctly by integrating information from floor sensors and the targetspecific information from multiple cameras. Integration of these two types of sensors enables complement of each weak point and the correct tracking of the target. Moreover, we develop a new particle filter framework that tracks the human head by using the estimated human body position simultaneously. Our experimental results demonstrate the effectiveness and robustness of the method against several complicated movements of multiple persons. The results also demonstrate that this method can maintain correct tracking when the targets are in close proximity.

[1]  F. Livesey,et al.  The ORL Acfive Floor , 1997 .

[2]  Justus H. Piater,et al.  Tracking by cluster analysis of feature points using a mixture particle filter , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[3]  Larry S. Davis,et al.  Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering , 2006, ECCV.

[4]  Tomomasa Sato,et al.  Multiple people tracking by integrating distributed floor pressure sensors and RFID system , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[5]  Neill W Campbell,et al.  IEEE International Conference on Computer Vision and Pattern Recognition , 2008 .

[6]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[7]  Yoichi Sato,et al.  3D Head Tracking using the Particle Filter with Cascaded Classifiers , 2006, BMVC.

[8]  M. D. Addlesee,et al.  The ORL Active Floor , 1997 .

[9]  Maja J. Mataric,et al.  A laser-based people tracker , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[10]  Rui Fukui,et al.  Expression method of human locomotion records for path planning and control of human-symbiotic robot system based on special existence probability model of humans , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[11]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Larry S. Davis,et al.  Fast multiple object tracking via a hierarchical particle filter , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  F. Livesey,et al.  The ORL active floor [sensor system] , 1997, IEEE Wirel. Commun..

[14]  Gregory D. Abowd,et al.  The Aware Home: A Living Laboratory for Ubiquitous Computing Research , 1999, CoBuild.

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

[16]  Hiroshi Ishiguro,et al.  Human tracking using floor sensors based on the Markov chain Monte Carlo method , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[17]  Andrea F. Cattoni,et al.  A particle filter based fusion framework for video-radio tracking in smart spaces , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.