An error compensation based background modeling method for complex scenarios

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.