Abstract
The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.
Highlights
The vast majority of animals, including humans, get the most information from vision among various sensory organs and with this vision, they recognize and judge the situation [1]
Two reactivity maps were combined and comparison evaluations were conducted on the UMN and Avenue datasets, which are a temporal saliency map was generated to detect the suspicious behavior regions
It was a difficult environment to method achieved a 100% true negative rate, because nothing was detected as a suspicious behavior measure the motion vector properly
Summary
The vast majority of animals, including humans, get the most information from vision among various sensory organs and with this vision, they recognize and judge the situation [1]. The technology of image processing and the performance of the computer have dramatically improved, analyzing and judging the situation comprehensively as a human does is still difficult [3]. As various technologies using image processing continue being developed, the scope of intelligent image security technology in the video security market is rapidly expanding; the market share is rapidly expanding from hardware to software, such as intelligent image analysis [4]. The technology used for image security requires suspicious behavior detection technology to prevent public security issues, incidents, and accidents. Attempting to enter a personal property, entering a subway station without paying a ticket, kidnapping a child, beating a person, or an act of sudden collapse of a person who is walking along the road may be examples
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