A taxonomy of wireless micro-sensor network models
ACM SIGMOBILE Mobile Computing and Communications Review
Power conservation and quality of surveillance in target tracking sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Surveillance camera scheduling: a virtual vision approach
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
An adaptive focus-of-attention model for video surveillance and monitoring
Machine Vision and Applications
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Safety security concerns drive the need to visually monitor large, complex environments (such as college campuses and office parks). Operators use networks of cameras to surveil areas in a dual fashion: first, identify events or targets of interest and second, monitor newly identified events and targets until a determination of subsequent action is made. An intelligent camera motion algorithm can assist operators with surveilling large areas by leveraging the region of interest configuration. This article compares the usefulness of camera motion planning by minimizing the time a space goes unseen (time based) against minimizing the size of contiguous unviewable space (space based). In addition, an autonomous, multi-camera, cooperative surveillance algorithm that operates a network of pan---tilt---zoom cameras to monitor a region of interest is presented. Coverage is accomplished via a novel algorithm that combines both time and space-based heuristics without an exhaustive search for the optimal parameters. This approach not only cooperatively manages camera coverage, but introduces a mechanism for balancing resource assignment between coverage to acquire new targets and tracking of previously discovered targets. Other benefits include a user-interface mechanism and occlusion awareness for expansion to cluttered urban environments. Experimental results are presented and compared for target acquisition and tracking in a typical urban campus environment. Results show that average linear uncovered length (ALUL) better predicts target acquisition performance. In addition, the use of ALUL for selection of camera panning angles improves target tracking and surveillance performance for both static and dynamic surveillance systems.