Statistical background subtraction based on imbalanced learning

In this paper, we study the class imbalance problem in statistical background subtraction. Firstly, we discuss the imbalance essence in background subtraction, and conclude that foreground and background are inherently imbalanced. Secondly, following the imbalanced learning strategy in machine learning, we present a spatio-temporal over-sampling method to resolve the class imbalance in background subtraction. Our method densely generate synthesized foreground samples in compact 3D spatio-temporal domain. Those generated samples could reduce the imbalance level between foreground and background from both quantity and quality, and therefore contribute to improvement of detection performance. We also define a new index to measure the change of imbalance level during over-sampling. Experiments are conducted on public datasets to demonstrate the effectiveness of our method.