Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization

In this paper we present a new method for object tracking initialization using background subtraction. We propose an effective scheme for updating a background model adaptively in dynamic scenes. Unlike the traditional methods that use the same “learning rate” for the entire frame or sequence, our method assigns a learning rate for each pixel according to two parameters. The first parameter depends on the difference between the pixel intensities of the background model and the current frame. The second parameter depends on the duration of the pixel being classified as a background pixel. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.