Real-time object detection in embedded video surveillance systems

In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.