Moving object detection is frequently used as a springboard for advanced computer vision analysis in complex scenes. Nevertheless, due to unstable changes in the background, most existing background model hardly maintain superior performance. To this concern, we propose a novel pixel-level background model that has three innovations. First, we introduce K-means to directly model the spatiotemporal dependencies between pixels. These dependencies are exploited to discover static core information in the high-frequency changing spatial domain, resulting in excellent property in dynamic backgrounds. Besides, the notion of complementarity is taken as a feature selection criterion. In multi-feature model, the ability to supervise each other between features is important in the ambiguity challenges, e.g., shadow. Finally, feature models recommend each other in the update mechanism, and the diffusion rate of effective information in each feature model can be maximized by finding the best candidate feature. By virtue of this mechanism, model can be updated efficiently when large background migration occurs, e.g., PTZ. Experimental results on some standard benchmarks show that SIM-MFR can achieve promising performance compared to some state-of-the-art approaches.
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