SEMI-SUPERVISED BACKGROUND SUBTRACTION OF UNSEEN VIDEOS: MINIMIZATION OF THE TOTAL VARIATION OF GRAPH SIGNALS

Recently, several successful methods based on deep neural networks have been proposed for background subtraction. These deep neural algorithms have almost perfect performance, relying in the availability of ground-truth frames of the tested videos during the training step. However, the performance of some of these algorithms drops significantly when tested on unseen videos. In this paper, concepts of semi-supervised learning are introduced in the problem of background subtraction for unseen videos. We propose a new algorithm named GraphBGS-TV, this method uses: Mask R-CNN for instances segmentation; temporal median filter for background initialization; motion, texture, and intensity features for representing the nodes of a graph; k-nearest neighbors for the construction of the graph; and finally a total variation minimization algorithm to solve the problem of background subtraction. GraphBGS-TV is tested in the change detection dataset, outperforming unsupervised and supervised methods in the challenges "PTZ" and "shadows".