Energy-based surveillance systems for ATM machines

This paper presents a video surveillance system which can detect and deal with typical abnormal behaviors on Automatic Teller Machine (ATM), such as fraud and robbery, etc. Based on the case study of violent incident video records, a weighted kinetic energy extraction approach for violence identification is proposed. By using the new approach, the motion field is weighted with angle coefficient, thus reducing the video stream to a one-dimension energy series. Experimental results show that the ATM video surveillance system with energy approach is effective for typical incident classification and that the corresponding alarm signal is reliable.