Adaptive Change Detection for Real-Time Surveillance Applications

This paper describes a new real-time approach for detecting changes in grey level image sequences, which were taken from stationary cameras. This new method combines a temporal difference method with an adaptive background model subtraction scheme. When changes in illumination occur, the background model is automatically adapted to suit the new conditions. For the adaptation of the background model a new method is proposed, which avoids reinforcement of adaptation errors by performing the adaptation solely on those regions that were detected by the temporal difference method rather than using the regions resulting from the overall algorithm. Thus the adaptation process is separated from the results of its own background subtraction algorithm. The change detector was successfully tested both in a vision-based workspace monitoring system for different kinds of non-autonomous service robots and in a surveillance scenario, in which it was the task to detect people in a subway-platform scenario. The proposed realtime algorithm showed recognition rates of up to 90% in the foreground and 84% in the background and performed in all cases at least 12% better than the alternative method of adaptive background estimation which uses a modified Kalman filtering technique.