Incorporating Deep Background Prior Into Model-Based Method for Unsupervised Moving Vehicle Detection in Satellite Videos

Chao Xiao, Ting Liu, Xinyi Ying, Yingqian Wang, Miao Li, Li Liu, Wei An, Zhijie Chen

Abstract: Background reconstruction is a key step of moving object detection in satellite videos. Most existing model-based methods exploit low-rank prior to recover background, which has achieved good performance but suffered degradation under complex and dynamic scenes. In this article, we introduce a deep background prior into model-based methods for moving vehicle detection in satellite videos. Our deep background prior is obtained by a background reconstruction network, which can learn to reconstruct the background from consecutive frames. By applying our deep background prior into model-based methods, a closed-form solution can be obtained via the alternating direction method of multipliers (ADMM), and then, detection results can be acquired through iterative optimization. More importantly, our background reconstruction network can be trained in an unsupervised way by introducing specifically designed loss, thus relieving the dependence on large-scale labeled datasets. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed method.