Effective visual surveillance with cooperation of multiple active cameras

This paper presents a nearly real-time surveillance system to track multiple moving objects by controlling multiple pan-tilt camera platforms. In order to describe the relationship between the targets and camera in this surveillance system, the input/output hidden Markov model (HMM) is applied here in the well-defined spherical camera coordinate. For the less number of cameras to effectively monitor a wide surveillance space, the overall cameras have to closely cooperate. We propose a hierarchical camera selection and task distribution strategy, and the action decision of each camera platform is according to its assigned role. Furthermore, an optimal camera action selection strategy is presented for one camera which is assigned to track multi-target within its limited field of view. The maximization of mutual information for the action design is evaluated by the Monte Carlo method. The overall performance has been validated in the experiments of real-time surveillance.

[1]  Joachim Denzler,et al.  Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Li-Chen Fu,et al.  Multi-Target Tracking using Separated Importance Sampling Particle Filters with Joint Image Likelihood , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Norimichi Ukita,et al.  Real-time multitarget tracking by a cooperative distributed vision system , 2002, Proc. IEEE.

[6]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[7]  Li-Chen Fu,et al.  Multiple People Visual Tracking in a Multi-Camera System for Cluttered Environments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  L. Hodge,et al.  Scalability and optimality in a multi-agent sensor planning system , 2004, Proceedings World Automation Congress, 2004..

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

[10]  Beno Benhabib,et al.  An Active Vision System for Multitarget Surveillance in Dynamic Environments , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Mubarak Shah,et al.  Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Mohamed S. Kamel,et al.  An agent-based approach to multisensor coordination , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[14]  A. Doucet,et al.  Particle filtering for multi-target tracking and sensor management , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[15]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[16]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.