Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.
Kaimin CHEN
Sichuan University
Wei LI
Sichuan University
Zhaohuan ZHAN
Sichuan University
Binbin LIANG
Sichuan University
Songchen HAN
Sichuan University
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Kaimin CHEN, Wei LI, Zhaohuan ZHAN, Binbin LIANG, Songchen HAN, "Camera Selection in Far-Field Video Surveillance Networks" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 3, pp. 528-536, March 2019, doi: 10.1587/transcom.2018EBP3079.
Abstract: Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3079/_p
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@ARTICLE{e102-b_3_528,
author={Kaimin CHEN, Wei LI, Zhaohuan ZHAN, Binbin LIANG, Songchen HAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Camera Selection in Far-Field Video Surveillance Networks},
year={2019},
volume={E102-B},
number={3},
pages={528-536},
abstract={Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.},
keywords={},
doi={10.1587/transcom.2018EBP3079},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - Camera Selection in Far-Field Video Surveillance Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 528
EP - 536
AU - Kaimin CHEN
AU - Wei LI
AU - Zhaohuan ZHAN
AU - Binbin LIANG
AU - Songchen HAN
PY - 2019
DO - 10.1587/transcom.2018EBP3079
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2019
AB - Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.
ER -