Object tracking initialization using automatic moving object detection

In this paper we present new methods for object tracking initialization using automated moving object detection based on background subtraction. The new methods are integrated into the real-time object tracking system we previously proposed. Our proposed new background model updating method and adaptive thresholding are used to produce a foreground object mask for object tracking initialization. Traditional background subtraction method detects moving objects by subtracting the background model from the current image. Compare to other common moving object detection algorithms, background subtraction segments foreground objects more accurately and detects foreground objects even if they are motionless. However, one drawback of traditional background subtraction is that it is susceptible to environmental changes, for example, gradual or sudden illumination changes. The reason of this drawback is that it assumes a static background, and hence a background model update is required for dynamic backgrounds. The major challenges then are how to update the background model, and how to determine the threshold for classification of foreground and background pixels. We proposed a method to determine the threshold automatically and dynamically depending on the intensities of the pixels in the current frame and a method to update the background model with learning rate depending on the differences of the pixels in the background model and the previous frame.