Abstract
In this paper, we study the important issues in the design of an efficient wireless real-time visual surveillance system (WISES). Two important considerations are to minimize: (1) the video workload on the wireless network; and (2) the processing workload at the front-end video capturing unit. To achieve the first objective, we propose a cooperative framework for semantic filtering of video frames instead of forwarding every video frame to the back-end server for analysis and monitoring query evaluation. To minimize the processing workload at the front-end unit, a hierarchical object model (HOM) is designed to model the status of the objects, and their temporal and spatial properties in the video scene. With the information provided from the back-end server, the front-end unit pre-analyses the current status of the objects in the HOM by comparing the selection conditions in the submitted monitoring queries following the adaptive object-based evaluation (APOBE) scheme which is proposed to reduce the processing workload at the front-end unit. In APOBE, a higher evaluation frequency is given to the object which is closer to satisfy the condition in the monitoring queries. The performance of WISES has been studied to demonstrate the efficiency of the proposed scheme.
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References
Barron J, Fleet D, Beauchemin S (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):42–77
Bobick F, Davis JW (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23(3):257–267
Chiasserini CF, Magli E (2002) Energy consumption and image quality in wireless video-surveillance networks. The 13th IEEE international symposium on personal, indoor and wireless radio communications 5:2357–2361, September
Chris D, Mahesh S, John H, Pradeep K (1999) Collaborative surveillance using both fixed and mobile unattended sensor platforms. In: Proceedings of SPIE’s 13th annual international conference on aerospace/defense sensing, simulation, and controls, vol 3713, pp 178–185, April
Collins RT, Lipton AJ, Kanade T (2000) A system for video surveillance and monitoring. In: Proceeding of 2000 conference on automated deduction (Springer LNAI 1831), pp 497–501, Pittsburgh, June
Grimson WEL, Stauffer C, Romano R, Lee L (1998) Using adaptive tracking to classify and monitor activities in a site. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 22–29
Huwer S, Niemann H (2000) Adaptive change detection for real-time surveillance applications. In: Proceedings of third IEEE international workshop on visual surveillance, pp 37–46, July
Intille S, Davis J, Bobick A (1997) Real-time closed-world tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 697–703, June
Piater JH, Crowley JL (2001) Multi-modal tracking of interacting targets using Gaussian approximations. In: Proceedings of second IEEE international workshop on performance evaluation of tracking and surveillance, PETS-2001, Kauai, December
Piater JH, Richetto S, Crowley JL (2002) Event-based activity analysis in live video using a generic object tracker. In: Proceedings of third IEEE international workshop on performance evaluation of tracking and surveillance, PETS-2002, Copenhagen, June
Wren Christopher, Azarbayejani Ali, Darrell Trevor, Pentland Alex (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785, July
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The work described in this paper was partially supported by a grant from CityU (Project No. 7001472).
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Lam, KY., Chiu, C.K.H. The design of a wireless real-time visual surveillance system. Multimed Tools Appl 33, 175–199 (2007). https://doi.org/10.1007/s11042-006-0056-9
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DOI: https://doi.org/10.1007/s11042-006-0056-9