DMCtrac: Distributed multi camera tracking

Advances in the field of computer vision enable smart cameras to cooperatively analyse scenes without human intervention. Large networks of autonomous, self-organising PTZ (pan, tilt, zoom) cameras require algorithms and protocols that make way for cooperation between multiple smart cameras (SCs). This paper introduces a distributed algorithm for object tracking with multiple SCs (DMCtrac). The focus lies on PTZ management issues arising in large SC systems rather than on computer vision algorithms. DMCtrac enables SCs to observe objects throughout an area under surveillance by using their PTZ abilities to follow these objects. The algorithm has been evaluated by simulation of SC systems with up to 50 SCs as used for example for people tracking and traffic analysis. Results show, that DMCtrac is able to cope with large numbers of SCs and objects and is robust towards real world disturbances like communication failure.

[1]  Nicu Sebe,et al.  Cooperative Object Tracking with Multiple PTZ Cameras , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[2]  Bernhard Rinner,et al.  Distributed embedded smart cameras for surveillance applications , 2006, Computer.

[3]  Giorgio Ventre,et al.  Network Simulator NS2 , 2008 .

[4]  Jörg Hähner,et al.  Towards Self-organising Smart Camera Systems , 2008, ARCS.

[5]  Jaswinder Singh,et al.  SCCS: A Scalable Clustered Camera System for Multiple Object Tracking Communicating Via Message Passing Interface , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[6]  Leonidas J. Guibas,et al.  Facenet: Tracking People and Acquiring Canonical Face Images in a Wireless Camera Sensor Network , 2007, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras.

[7]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[8]  Jörg Hähner,et al.  Spatial Partitioning in Self-Organizing Smart Camera Systems , 2008, IEEE Journal of Selected Topics in Signal Processing.

[9]  Patrick Chisan Hew.  Visualisation of Surveillance Coverage by Latency Mapping , 2003, InVis.au.

[10]  Bernhard Rinner,et al.  Autonomous Multicamera Tracking on Embedded Smart Cameras , 2007, EURASIP J. Embed. Syst..

[11]  Wayne H. Wolf.  Distributed Peer-to-Peer Smart Cameras: Algorithms and Architectures , 2005, ISM.

[12]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[13]  Juha Röning,et al.  Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision , 1992 .

[14]  Mongi A. Abidi,et al.  Real-time video tracking using PTZ cameras , 2003, International Conference on Quality Control by Artificial Vision.

[15]  Hartmut Schmeck,et al.  Towards a quantitative notion of self-organisation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[16]  Shiuh-Ku Weng,et al.  Video object tracking using adaptive Kalman filter , 2006, J. Vis. Commun. Image Represent..

[17]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[18]  Kirstie L. Bellman,et al.  Visual surveillance coverage: strategies and metrics , 2005, SPIE Optics East.

[19]  Ian F. Akyildiz,et al.  A survey on wireless mesh networks , 2005, IEEE Communications Magazine.

[20]  Pankaj,et al.  Object Matching Across Multiple Non-overlapping Fields of View Using Fuzzy Logic , 2006 .