Adaptive Background Model for Arbitrary-Long Stationary Target

Background reconstruction plays an important role in many applications like video surveillance, motion analysis. Traditional Adaptive Gaussian Mixture Model will lose target when deal with arbitrary-long stationary object. In this paper, a novel method for detecting this kind of object is proposed to improve the performance of Adaptive Gaussian Mixture Model. Parameter restoration is designed to deal with arbitrary-long stationary target and solve the short-comings of the latest algorithm. The parameters among the K distribution of each pixel covered by the object will be restored when the object stayed for over threshold frames. Then the target will not be updated as a part of background model. Experimental results show that the proposed algorithm proves to be a more robust method by detecting the stationary target in an arbitrary-long time.