The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
감시용 카메라 네트워크가 극도로 밀도가 높아짐에 따라 다양한 카메라에서 가장 유익하고 바람직한 뷰를 찾는 것이 점점 더 중요해지고 있습니다. 본 논문에서는 가장 선명한 가시성을 제공하고 원거리 감시 대상을 정확하게 포착하는 카메라를 선택하는 목표를 달성하기 위한 카메라 선택 방법을 제안합니다. 우리는 이미지 가시성과 서로 다른 카메라 간의 타겟 일치 정도를 고려한 이점 함수를 설계합니다. 여기서 가시성은 강도 히스토그램 분포의 엔트로피를 사용하여 정의되며, 목표 대응은 측광 기능이 아닌 활동 기능을 기반으로 합니다. 제안된 솔루션은 인공 환경과 실제 환경 모두에서 테스트되었습니다. 성능 평가 결과, 우리의 타겟 대응 방식이 원거리 감시에 잘 맞는 것으로 나타났으며, 제안한 선택 방식은 기존 방식보다 감시 타겟을 정확하게 포착하는 카메라를 식별하는 데 더 효과적이었다.
Kaimin CHEN
Sichuan University
Wei LI
Sichuan University
Zhaohuan ZHAN
Sichuan University
Binbin LIANG
Sichuan University
Songchen HAN
Sichuan University
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부
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
부
@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},}
부
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 -