Motion Detection and Tracking of Multiple Objects for Intelligent Surveillance

In this paper proposes new strategies for object tracking initialization using automatic moving object detection based background subtraction. The new strategies are integrated into the real-time object tracking system. The proposed background model updating technique and adaptive thresholding are used to produce a foreground object mask for object tracking initialization. Traditional background subtraction technique detects moving objects by subtracting the background model from the current image. Compare to various common moving object detection technique, background subtraction segments foreground objects more accurately and detects foreground objects even if they are non moving. However, one drawback of traditional background subtraction is that it is vulnerable environmental changes, for instance, gradual or fast illumination changes. The reason of this disadvantage is that it assumes a static background, and therefore a background model update is needed for dynamic backgrounds. The most important challenges are how to update the background model, and how to find out the threshold for classification of foreground and background pixels. The proposed technique is to determine automatically and dynamically depending on the intensities of the pixels within the current frame and a technique to update the background model with learning rate depending on the variations of the pixels within the background model and also the previous frame. This paper additionally represented a shape tracking technique to track the moving multiple objects in surveillance video.