Abstract
For persistent objective tracking based on camera sensor networks, sensor deployment and scheduling are challenging problems especially in indoor environments. In this paper, we focus on the indoor objective tracking in three-dimensional (3D) camera sensor networks (CSNs) and aim to design the scheduling strategies to guarantee the coverage quality of the objective trajectory. We introduce the active-period-minimizing scheduling problem in CSNs for indoor objective tracking, with the goal of minimizing the total active durations of all the sensors. To solve the problem, we propose a pair of projection scheduling algorithms from the perspectives of the sensors and the trajectory correspondingly. To evaluate the algorithm performance on the time efficiency, we conduct extensive simulation experiments and their results indicate the proposed algorithms’ advantages on time efficiency.
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Acknowledgment
This research was partly supported by National Natural Science Foundation of China under Grant 11671400 and 61672524, General Project of Science and Technology Plan of Beijing Municipal Education Commission (KM201910017006), Program of Beijing Excellent Talents Training for Young Scholar (2016000020124G056).
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Hong, Y., Wang, Y., Zhu, Y., Li, D., Chang, M., Chen, Z. (2019). Projection-Based Coverage Algorithms in 3D Camera Sensor Networks for Indoor Objective Tracking. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_34
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DOI: https://doi.org/10.1007/978-3-030-34980-6_34
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