WeSamBE: A Weight-Sample-Based Method for Background Subtraction

Background subtraction techniques are often treated as fundamental and significant ways to analyze and understand video content. In this paper, we propose a weight-sample-based method for foreground detection. This method allows us to use a few samples with variable weights to achieve effective change detection. To rapidly adapt to changing scenarios, a minimum-weight update policy is first proposed to replace the most inefficient sample instead of the oldest sample or a random sample. In addition, a reward-and-penalty weighting strategy is put forward to reinforce active samples and punish others. In this way, the weights of relatively effective samples are increased and the false updating of effective samples with smaller weights is reduced. Moreover, some other strategies, such as spatial-diffusion policy and random time subsampling, are also incorporated to ensure the flexibility of the proposed method. Finally, in our experiments, an adaptive feedback technique is incorporated into our algorithm to adapt to more challenging videos, and the final results indicate that our method is superior to the state-of-the-art approaches on the challenging CDnet data set.