Robust background extraction scheme using histogram-wise for real-time tracking in urban traffic video

Detecting moving objects in video sequence with a lot of moving vehicles and other difficult conditions is a fundamental and difficult task in many computer vision applications. A common approach is based on background extraction, which identifies moving objects from the input video frames that differs significantly from the background model. There are many challenges exist in extracting a good background. Our proposed method is able to deal with illumination change based on homomorphic filer. Using histogram- wise makes the background model be actively reacted to the changes in background such as starting and stopping of vehicles. Moreover, the identification of stationary objects such as swinging leaves, rain, snow, lanes, shadows, can make the background model more robust and also can reduce the processing time. The proposed method is simplicity and effectiveness by a simple technique to more sophisticated probabilistic modeling techniques. The algorithm has been successful tested in busy traffic and other difficult conditions scenes with the comparisons to some basic and complex probabilistic algorithms.