Autonomous multi-camera tracking using distributed quadratic optimization

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Abstract

Multi-camera object tracking is an efficient approach commonly used in security and surveillance systems. In a conventional multi-camera setup, a central computational unit processes large amounts of data in real time that is provided by distributed cameras. High network traffic, cost of storage on the central unit, scalability of the system, and vulnerability of the central unit to attacks are among the disadvantages of such systems. In this paper, we present an autonomous multi-camera tracking system to overcome these challenges. We assume cameras that are capable of limited computation for locally tracking a subset of objects in the scene, as well as peer-to-peer network connectivity among the cameras with a decent bandwidth that is sufficient for message passing to achieve coordination. We propose an efficient distributed algorithm for coordination and load-balancing among the cameras. We also provide experimental results to validate the utility of the proposed algorithm in comparison to a centralized algorithm.

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Osmanlıoğlu, Y., Shakibajahromi, B., & Shokoufandeh, A. (2018). Autonomous multi-camera tracking using distributed quadratic optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10746 LNCS, pp. 175–188). Springer Verlag. https://doi.org/10.1007/978-3-319-78199-0_12

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