Memorizing GMM to Handle Sharp Changes in Moving Object Segmentation
Yanjiang Wang, Peng Suo, Yujuan Qi · 2009
Gaussian mixture model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails in segmenting moving objects when the scene changes sharply. To handle this problem, a novel background modeling algorithm - memorizing GMM is proposed, which is inspired by the way human perceive the environment. It can make the GMM remember what the scene has ever been during the learning and updating period. Experimental results show that it can help segmenting moving objects precisely when the scene changes sharply.