Face tracking by using omnidirectional sensor network

In recent years, security camera systems have been installed in various public facilities. More intelligent processes are needed to track people in image sequences for security camera systems. In this paper, we propose a face tracking and recognition method based on a Bayesian framework. We assume that an observed space is three-dimensional, and we estimate the 3D position of a person. We use facial 3D shape, movement, and texture models for face tracking and recognition. Omnidirectional image sensors are used to acquire image sequences of two pedestrians because the sensors have a wide view and are suitable for object tracking. Our system generates 3D positional hypotheses based on the facial movement model and these positional hypotheses are projected onto an image plane. Image features are extracted from projected hypotheses and the system distinguishes faces using these image features. Our evaluation experiments show that our proposed method is effective for face tracking, and that tracking accuracy is proportional to the number of cameras used.

[1]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[3]  Ming Liu,et al.  Robust Multi-View Multi-Camera Face Detection inside Smart Rooms Using Spatio-Temporal Dynamic Programming , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[4]  Tieniu Tan,et al.  Principal axis-based correspondence between multiple cameras for people tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Isaac Cohen,et al.  Jeju Island , Korea TRACKING PEOPLE IN CROWDED SCENES ACROSS MULTIPLE CAMERAS , 2004 .

[6]  Hiroshi Ishiguro,et al.  Multi-hypothesized Oscillation Models Employing Floor Sensors for Tracking People , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  Hiroshi Ishiguro,et al.  Laser tracking of human body motion using adaptive shape modeling , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Bernhard Rinner,et al.  Visual on-line learning in distributed camera networks , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[9]  Larry S. Davis,et al.  Unified multi-camera detection and tracking using region-matching , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[10]  Mubarak Shah,et al.  Tracking across multiple cameras with disjoint views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Jake K. Aggarwal,et al.  Object tracking in an outdoor environment using fusion of features and cameras , 2006, Image Vis. Comput..

[12]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[13]  Yasushi Yagi,et al.  Omnidirectional imaging with hyperboloidal projection , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).