A Framework for Distributed Human Tracking

Today, more than ever, monitoring and surveillance systems play an important role in many aspects of our lives. Technology plays a vital role in our efforts to create, store and analyze vast amounts of data for both security and commercial purposes. In this paper, we propose an application which combines research performed in computer networks, multimedia databases and computer vision. We consider the problem where a number of networks are interconnected. Each of the individual nodes (networks) are collecting, processing and storing data from several sensors (cameras). Specifically, we emphasize on how the data (images) are processed by the individual nodes and how the information is transmitted, so that queries involving multiple nodes can be answered. During this process, we also identify several challenges related to sharing voluminous content provided by visual surveillance devices.

[1]  Jake K. Aggarwal,et al.  Automatic tracking of human motion in indoor scenes across multiple synchronized video streams , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  L. Davis,et al.  W 4 S: a Real-time System for Detecting and Tracking People in 2 1 2 D , 1998 .

[3]  Jussi Kangasharju,et al.  Secure and resilient peer-to-peer e-mail design and implementation , 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003).

[4]  L. Davis,et al.  M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene , 2003, International Journal of Computer Vision.

[5]  Jake K. Aggarwal,et al.  Tracking human motion in an indoor environment , 1995, Proceedings., International Conference on Image Processing.

[6]  Larry S. Davis,et al.  W4S : A real-time system for detecting and tracking people in 2 D , 1998, eccv 1998.

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

[8]  Larry S. Davis,et al.  Hydra: multiple people detection and tracking using silhouettes , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[9]  Michael J. Black,et al.  Cardboard people: a parameterized model of articulated image motion , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[10]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Jake K. Aggarwal,et al.  Tracking Human Motion in Structured Environments Using a Distributed-Camera System , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[13]  Jake K. Aggarwal,et al.  Tracking human motion using multiple cameras , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Trevor Darrell,et al.  Integrated Person Tracking Using Stereo, Color, and Pattern Detection , 2000, International Journal of Computer Vision.

[16]  Antony I. T. Rowstron,et al.  Squirrel: a decentralized peer-to-peer web cache , 2002, PODC '02.

[17]  J. Krumm,et al.  Multi-camera multi-person tracking for EasyLiving , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[18]  Arjen P. de Vries,et al.  The Mirror MMDBMS Architecture , 1999, VLDB.

[19]  Bin Yao Building an interoperable distributed image database management system , 2000 .