Affiliations: Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia
Correspondence:
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Corresponding authors: Guang Ting Foo, Kam Meng Goh, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan Genting Kelang, Kuala Lumpur 53300, Malaysia. E-mails: foogt@hotmail.my; gohkm@tarc.edu.my.
Abstract: Having an autonomous system to alarm for violence or suspicious incidence could greatly strengthen the security system. Such autonomous system could also be useful for other application such as patient monitoring, retail shop, and children surveillance. However, the current technology has not yet reach the level to effectively analyze the video since currently most video surveillance system could not understand the events happen in the video. Complex changes in environment caused by camera motion, dynamic scene such as crowds, changes in lighting intensity, viewing from different angles, wide variation in spatial (e.g. size of interest subject relative to video) and temporal (speed of the subjects in performing actions) make video analysis task a very challenging task. Even with these difficulties, researches in improving video analysis methods are still being actively explored. Some research approaches in violence incidence detection resembling the method used in detecting abnormal incidence. Instead of detecting whether an incidence have occurred, we attempt to build a model to detect the actions related to violence. In this paper, an online detection model is built to detect specific action related to violence actions. The model is built with reference of the image object detection (Faster-Region Convolution Neural Network, Faster-RCNN) and video action detection (Tube-Convolution Neural Network, TCNN).
