N-Ocular stereo for real-time human tracking

In recent years, various systems using multiple vision sensors have been proposed in the area of computer vision. For example, several systems track people or automobiles in the real environment with multiple vision sensors [32, 141, 169, 51, 189] while other systems analyze their behaviors [89]. Compared with systems using a single vision sensor [243, 116, 140, 156], these systems are able to observe a moving target in a large space for a long time. However, they need to use many vision sensors to seamlessly cover the entire space, since a single standard vision sensor itself has a narrow range of view. On the other hand, an omnidirectional vision sensor (ODVS) provides a wide field of view of up to a 360° at a time. In addition, use of multiple ODVSs provide rich and redundant visual information, which enables robust recognition of the targets. Thus, multiple ODVSs opens up a new application area of computer vision with their wide range of view.

[1]  R. Collins,et al.  Using a DEM to Determine Geospatial Object Trajectories , 1999 .

[2]  Hiroshi Ishiguro,et al.  Omni-Directional Stereo , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[4]  Jakub Segen,et al.  A camera-based system for tracking people in real time , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Marwan A. Simaan,et al.  An efficient algorithm for tracking the angles of arrival of moving targets , 1991, IEEE Trans. Signal Process..

[6]  Hiroshi Ishiguro,et al.  Development of Low-Cost Compact Omnidirectional Vision Sensors and their applications , 1998 .

[7]  Robert T. Collins,et al.  Cooperative Multi-Sensor Video Surveillance , 1999 .

[8]  David C. Hogg,et al.  Learning the Distribution of Object Trajectories for Event Recognition , 1995, BMVC.

[9]  Takeo Kanade,et al.  A multiple-baseline stereo , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Takeo Kanade,et al.  A stereo machine for video-rate dense depth mapping and its new applications , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Yaakov Bar-Shalom,et al.  Tracking methods in a multitarget environment , 1978 .

[12]  C. Morefield Application of 0-1 integer programming to multitarget tracking problems , 1977 .

[13]  Y. Bar-Shalom,et al.  A new relaxation algorithm and passive sensor data association , 1992 .

[14]  W. Eric L. Grimson,et al.  Using adaptive tracking to classify and monitor activities in a site , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[15]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  P. Smith,et al.  A branching algorithm for discriminating and tracking multiple objects , 1975 .

[17]  Jeffrey E. Boyd,et al.  MPI-Video infrastructure for dynamic environments , 1998, Proceedings. IEEE International Conference on Multimedia Computing and Systems (Cat. No.98TB100241).

[18]  S. Shams Neural network optimization for multi-target multi-sensor passive tracking , 1996 .

[19]  Hiroshi Mizoguchi,et al.  Action recognition system based on human finder and human tracker , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.