Adaptive local spatial modeling for online change detection under abrupt dynamic background
Dong Liang, Shun’ichi Kaneko, Han Sun, Bin Kang · 2017
Change detection is an important theme in video processing. To provide reliable detection results in challenging scenes, traditional methods introduced sophisticated statistical distributions and handcraft spatial features to build background models. In this paper, we develop an intuitive background model based on simple statistical distribution and adaptive spatial correlation among pixels: For each observed pixel, we select a group of supporting pixels with high correlation, and then employ a single Gaussian to model the intensity deviations of each pixel pair. To compensate camera motion and fast adapt to dynamic pattern that coming afterwards, a randomized multichannel on-line updating mechanism is introduced. This observation is robust to abrupt illumination variation and dynamic background. Experimental results using all the video sequences provided by three challenging benchmarks (CDW-2012, CDW-2014 and SABS) validate it outperforms many state-of-the-art methods under various situations.