Face cataloger: multi-scale imaging for relating identity to location

The level of security at a facility is directly related to how well the facility can keep track of "who is where". The "who" part of this question is typically addressed through the use of face images for recognition either by a person or a computer face recognition system. The "where" part of this question can be addressed through 3D position tracking. The "who is where" problem is inherently multi-scale, wide angle views are needed for location estimation and high resolution face images for identification. A number of other people tracking challenges like activity understanding are multiscale in nature. An effective system to answer "who is where?" must acquire face images without constraining the users and must closely associate the face images with the 3D path of the person. Our solution to this problem uses computer controlled pan-tilt-zoom cameras driven by a 3D wide-baseline stereo tracking system. The pan-tilt-zoom cameras automatically acquire zoomed-in views of a person's head, while the person is in motion within the monitored space.

[1]  Nikos Paragios,et al.  Video-Based Surveillance Systems , 2002, Springer US.

[2]  Jake K. Aggarwal,et al.  Segmentation and recognition of continuous human activity , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[3]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[4]  Larry S. Davis,et al.  A Robust Background Subtraction and Shadow Detection , 1999 .

[5]  Ioannis T. Pavlidis,et al.  A video-based surveillance solution for protecting the air-intakes of buildings from chem-bio attacks , 2002, Proceedings. International Conference on Image Processing.

[6]  David Harwood A statistical approach for real time robust background subtraction , 1999 .

[7]  Mohan M. Trivedi,et al.  A WIDE AREA TRACKING SYSTEM FOR VISION SENSOR NETWORKS , 2002 .

[8]  Trevor Darrell,et al.  Plan-view trajectory estimation with dense stereo background models , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Helder Araújo,et al.  A surveillance system combining peripheral and foveated motion tracking , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[10]  Myron Flickner,et al.  Detection and tracking of shopping groups in stores , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  R. Pearl Biometrics , 1914, The American Naturalist.

[12]  A. Murat Tekalp,et al.  Feature extraction for the analysis of gait and human motion , 2002, Object recognition supported by user interaction for service robots.

[13]  Irfan Essa,et al.  Tracking Multiple People with Multiple Cameras , 1998 .

[14]  N. Paragios,et al.  Video-Based Surveillance Systems: Computer Vision and Distributed Processing , 2001 .

[15]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[16]  Rama Chellappa,et al.  A bayesian approach to simultaneous motion estimation of multiple independently moving objects , 2002, Object recognition supported by user interaction for service robots.

[17]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.