3D camera sensor scheduling algorithms for indoor multi-objective tracking

Yi Hong (), Yongcai Wang, Yuqing Zhu, Deying Li, Zhibo Chen and Jing Li
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Yi Hong: Beijing Forestry University
Yongcai Wang: Renmin University of China
Yuqing Zhu: California State University Los Angeles
Deying Li: Renmin University of China
Zhibo Chen: Beijing Forestry University
Jing Li: Beijing Institute of Petrochemical Technology

Journal of Combinatorial Optimization, 2020, vol. 39, issue 3, No 14, 899-914

Abstract: Abstract With the advantages of sensing data’s diversity, camera sensor networks (CSNs) have been applied to objective tracking in both outdoor and indoor environments. In indoor objective tracking, the objectives’ moving mode and sensors’ deployment have limitation and particularity, which bring more challenges on persistent monitoring for CSNs. In this paper, we consider the indoor multi-objective tracking in three-dimensional (3D) CSNs and focus on the 3D camera sensor scheduling for objective tracking to improve the coverage quality of the objective trajectory and minimizing the whole working periods. We firstly introduce the active-period-minimizing scheduling problem in CSNs for indoor objective tracking, with the goal of minimizing the total active periods of sensors. We solve the problem via three algorithms: the first algorithm is designed based on our proposed projection-based algorithm for single-objective case; the second one is proposed with the main idea of path coloring and the third one is a divide-and-conquer strategy with an approximation ratio of $$H(\frac{area(\mathcal {B})}{gside^2})$$H(area(B)gside2). To evaluate these algorithms’ performance on the time efficiency, we conduct extensive simulation experiments and analyze their results on the time efficiency advantages and applicable scenarios.

Keywords: Camera sensor networks; Indoor multi-objective tracking; Coverage quality; Active-period-minimizing scheduling (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10878-020-00532-0

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