Automatic Camera Selection and Fusion for Outdoor Surveillance under Changing Weather Conditions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
A survey on wireless multimedia sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Collaborative signal processing for target tracking in distributed wireless sensor networks
Journal of Parallel and Distributed Computing
Dynamic Sensor Selection for Single Target Tracking in Large Video Surveillance Networks
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Color-Based multiple agent tracking for wireless image sensor networks
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Dynamic sensor collaboration via sequential Monte Carlo
IEEE Journal on Selected Areas in Communications
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Aiming at fulfilling the wide-area video surveillance, this paper presents a cooperative multi-camera target tracking method for wireless camera sensor networks. In the proposed method, target detection is carried out by single-node processing based on background subtraction, whereas target tracking is performed by senor nodes cooperation based on automatic node selection. The main contributions of the proposed method are summarized as follows. First, each camera node uses an adaptive Gaussian mixture model to extract moving targets and an unscented Kalman filter to solve target tracking. Second, the correspondence between the targets in different camera views is established by homography transformation of target positions. Third, a confidence measure based on the size of the detected target blob and the estimate uncertainty of tracking is defined to achieve optimal node selection. Experimental results show that the proposed method can effectively select camera node to implement the accurate tracking in real scenes.