Background Subtraction via 3D Convolutional Neural Networks
Yongqiang Gao, Huayue Cai, Xiang Zhang, Long Lan, Zhigang Luo · 2018
Background subtraction can be treated as the binary classification problem of highlighting the foreground region in a video whilst masking the background region, and has been broadly applied in various vision tasks such as video surveillance and traffic monitoring. However, it still remains a challenging task due to complex scenes and for lack of the prior knowledge about the temporal information. In this paper, we propose a novel background subtraction model based on 3D convolutional neural networks (3D CNNs) which combines temporal and spatial information to effectively separate the foreground from all the sequences in an end-to-end manner. Different from conventional models, we view background subtraction as three-class classification problem, i.e., the foreground, the background and the boundary. This design can obtain more reasonable results than existing baseline models. Experiments on the Change Detection 2012 dataset verify the potential of our model in both quantity and quality.