Action Recognition Through Surveillance Camera Using Machine Learning Algorithm

Foo, Guang Ting (2018) Action Recognition Through Surveillance Camera Using Machine Learning Algorithm. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Surveillance cameras are widely used in surveillance for various kind of applications such as security, patient condition, retail shop and children surveillance. The availability video data will keep on expanding, the demand of analyzing video data will also keep on increasing(Tran et al., 2016). Furthermore the significance of smart or autonomous surveillance system could provide human a lot of convenience in term of reducing tedious human surveillance tasks, interaction with system gesture and video description tagging for further analysis. However, the current technology has not yet reach the level to effectively analyze 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, implementation of video analysis in actual applications are still slowly taking place now. In this project, a short review will be made on current state of art approaches to evaluate their performance. Then followed by an attempt to implement and modify the machine models of interest for autonomous surveillance application that is capable of recognizing slightly more complex actions with the new machine design.