Automatic video surveillance for theft detection in ATM machines: An enhanced approach

This paper deals with the development of an application for automation of video surveillance in ATM machines and detect any type of potential criminal activities that might be arising with the system which would considerably decrease the inefficiency that are existing in the prevalent systems. An advanced digital Image processing technique along with the combination of computer vision and unsupervised machine learning techniques would be utilized which would create phenomenal results in the detection of the activities and their categorization. The proposed system makes efficient utilization of vector graphics and lists out an effective algorithm which comprises of methodologies like background modelling, subtraction, identification of salient objects, tracking of those objects and finally ending up with the detection and identification of the necessary action for the prevention of such type of activities. The proposed system also indulges in semi-supervised learning techniques and uses matching techniques like pose clustering and consistency in order to train the system and develop it to be an automated system as a whole. Since the captured video is fragmented into smaller frames and then the vector graphics and image processing techniques are implemented, the entire mechanism takes place in real time decreasing the time complexity to a great extent making the system an efficient mechanism to prevent such anti-social activities.