An adaptive background model initialization algorithm with objects moving at different depths

Background subtraction is an essential element in most object tracking and video surveillance systems. The success of this low-level processing step is highly dependent on the quality of the background model maintained. Gutchess et al. [4] proposed a novel background initialization algorithm that utilizes local optical flow information to locate the stable interval (of intensity values) which is most likely to display background. However, it is found that the accuracy of the computed background is rather sensitive to the parameters used. In addition, their algorithm is not able to handle the scenario where objects are moving at different depths. In this paper, we propose an algorithm which is adaptive to the input sequence and is able to equalize the uneven effect caused by different object depths. Our algorithm is successfully tested on complex indoor and outdoor scenes with promising results.