Adaptive maintenance scheme for codebook-based dynamic background subtraction

摘要

We propose a novel adaptive maintenance scheme for the codebook-based background subtraction algorithm. With this technique, the accuracy and efficiency of the model are significantly improved. In the proposed method, we develop an equal-qualification updating strategy to replace the maximum-negative-run-length-based filtering strategy. Further, we substitute the cache-based foreground learning process with a random updating scheme. These modifications not only preserve the accuracy of the codebook model but also significantly reduce the number of parameters used in the maintenance scheme. In the modified framework, parameters that are scenario-sensitive are identified through extensive experiments and analysis. Then, adaptive methods are proposed for them. The proposed method ensures the best performance of the system across a variety of complex scenarios. In our experiments, comparisons are provided to confirm that the performance of the codebook model is significantly improved owing to the adaptive technique. The overall performance of the proposed method is evaluated against more than 20 state-of-the-art methods using several modern datasets. It is demonstrated that, despite using only color information, the proposed method outperforms the majority of the solutions by a significant margin.