Right click to open a feedback form in a new tab to let us know how this document benefits you. While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. The most flexible formulation of finding the optimal camera placement is based on a binary integer programming (BIP) problem. Despite the flexibility, most of the resulting BIP problems are NP-hard and any such formulations of reasonable size are not amenable to exact solutions. There exists a myriad of approximate algorithms for BIP problems, but their applications, efficiency, and scalability in solving camera placement are poorly understood. Thus, we develop a comprehensive framework in comparing the merits of a wide variety of approximate algorithms in solving the optimal camera placement problems. We first present a general approach of adapting these problems into BIP formulations. Then, we demonstrate how they can be solved using different approximate algorithms including greedy heuristics, Markov-chain Monte Carlo, simulated annealing, and linear and semidefinite programming relaxations. The accuracy, efficiency, and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method. Follow this and additional works at
Zhao et al. REF put forward a framework to compare the accuracy, efficiency and scalability of the greedy-based method, heuristics-based method, sampling-based method and LP and SDP relaxation-based method.
Approximate techniques in solving optimal camera placement problems
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