A Moving Object Detection Algorithm Based on Improved GMM and Short-term Stability Measure

In traditional Gaussian Mixture Modeling (GMM) algorithm, the risk that foreground model changes into background model rises with the cumulating of model weight under certain learning rate. That makes the algorithm unable to deal with slow moving object. This paper proposes an algorithm which takes advantage of the foreground models and employs an index of short-term stability measure to make a compound judgment. Each pixel status is decided real-timely considering the information of moving objects contained in foreground models and the pixel-level stability status. The results from different experiments verify that the proposed algorithm achieves a higher detection rate in detecting slow moving objects.