Compensating foreground error with background information usually helps to build an accurate background model for the subspace learning based background modeling method. However, dynamic background (swaying tree or waving water surface) and complex foreground signal may have bad influences on the compensation process. To solve the problem, we propose an error compensation based incremental subspace method for background modeling, which aims to build an accurate background model in complex scenarios. First, we bring a spatial continuity constraint to the foreground error estimation process, which helps to preserve more dynamic background information and increase the accuracy of the background model. Second, we formulate the foreground estimation task into a convex optimization problem, and design an accurate optimization algorithm and a fast optimization algorithm, respectively for different applications. Third, an alpha-mating based error compensation strategy is designed, which increases the anti-interference performance of our algorithm. At last, a median background template which does not rely on background model is constructed, which increases the robustness of our algorithm. Multiple experiments show that the proposed method is able to model background accurately even in complex scenarios, demonstrating the anti-interference performance and the robustness of our method.

社会治安状况的复杂态势对平安建设和社会治安治理工作提出了更高的要求.为了使治安工作更加贴近人民群众的需求,反应人民群众的切身利益,这种反映社会治安工作成效的“平安指数模型”,是以提高群众治安满意度为根本出发点和落脚点,通过“灰色系统理论”的数学方法,建立反应警情与群众满意度数据之间的关系数学模型,探索警情与群众满意度之间定量关系,定义平安指数的具体形式,并以深圳市宝安区的实际数据进行了测算验证.