Communication Dans Un Congrès Année : 2020

Semi-Supervised Background Subtraction Of Unseen Videos: Minimization Of The Total Variation Of Graph Signals

Résumé

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 Graph-BGS-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”

Fichier non déposé

Dates et versions

hal-02958697 , version 1 (06-10-2020)

Identifiants

Citer

Jhony Giraldo, Thierry Bouwmans. Semi-Supervised Background Subtraction Of Unseen Videos: Minimization Of The Total Variation Of Graph Signals. 2020 IEEE International Conference on Image Processing (ICIP), Oct 2020, Abu Dhabi, United Arab Emirates. pp.3224-3228, ⟨10.1109/ICIP40778.2020.9190887⟩. ⟨hal-02958697⟩

Collections

81 Consultations
0 Téléchargements

Altmetric

Partager

  • More