A fuzzy model-based integration framework for vision-based intelligent surveillance systems

Wahyono, Wahyono; Filonenko, Alexander; Kurnianggoro, Laksono; Jo, Kang-Hyun

doi:10.1109/icmech.2017.7921131

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…Ref. [12] addresses a framework for event decision of vision-based intelligent surveillance system based on the fuzzy model. A shape-perceived algorithm using the building block-based matching method is presented in [13] for object tracking of intelligent surveillance applications.…”

Section: Introductionmentioning

confidence: 99%

Particle-filter-based intelligent video surveillance system

Tsai

Wang

et al. 2016

2016 International Conference on System Science and Engineering (ICSSE)

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In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience.

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Section: Introductionmentioning

confidence: 99%