A signal detection theory approach for camera tamper detection

Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera and to detect tampers. Signal activity is used as a feature to measure the amount of information in the image. The distribution of features representing the normal operation of a camera are modeled as a Gaussian mixture model (GMM). The GMM is trained using synthetic data. To reduce the effects of noise, a Kalman filter is used to model changes in signal activity in the video. Experimental results show that the proposed approach out performed the state-of-the-art [13] in detecting tampered images with higher accuracy while generating lower false alarms.