Moving Object Detection via Integrating Spatial Compactness and Appearance Consistency in the Low-Rank Representation

Low-rank and sparse separation models have been successfully applied to background modeling and achieved promising results on moving object detection. It is still a challenging task in complex environment. In this paper, we propose to enforce the spatial compactness and appearance consistency in the low-rank and sparse separation framework. Given the data matrix that accumulates sequential frames from the input video, our model detects the moving objects as sparse outliers against the low-rank structure background. Furthermore, we explore the spatial compactness by enforcing the consistency among the pixels within the same superpixel. This strategy can simultaneously promote the appearance consistency since the superpixel is defined as the pixels with homogenous appearance nearby the neighborhood. The extensive experiments on public GTD dataset suggest that, our model can better preserve the boundary information of the objects and achieves superior performance against other state-of-the-arts.