Real-Time Automatic Camera Sabotage Detection for Surveillance Systems

Abstract

Video surveillance is very common for security monitoring of premises and sensitive installations. Criminals tamper the surveillance camera settings so that their (criminal) activities in the scene are not recorded properly, thereby making the captured video frames useless. Various camera tampering/sabotage include - changing the normal view of the camera by turning the camera away from the scene, obstructing the camera lens by placing some objects in front of the camera or spraying paint on it and defocusing the camera lens by changing the camera focus settings, spraying water or some viscous fluid on it. Manual monitoring of the surveillance systems have many limitations - human fatigue, lack of continuous monitoring, etc. Hence, real-time automated analysis and detection of suspicious events have gained importance. In this paper, we propose an efficient algorithm for camera tamper detection based on background modeling, edge details, foreground object size and its movement. In our testing or experimental setup, the results are encouraging with high precision and low false alarm rate. As the proposed method can process $320 \times 240$ resolution videos at $60-70$ frames/sec, it can be implemented for real-time applications.