BScGAN: DEEP BACKGROUND SUBTRACTION WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | BScGAN: DEEP BACKGROUND SUBTRACTION WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Bakkay M.C, Rashwan H.A, Salmane H., Khoudour L., Puig D., Ruichek Y. |
| Conference Name | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
| Publisher | Inst Elect & Electron Engneers; IEEE Signal Processing Soc |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-4799-7061-2 |
| Mots-clés | Background subtraction, Change detection, Deep learning, Generative Adversarial Networks |
| Résumé | This paper proposes a deep background subtraction method based on conditional Generative Adversarial Network (cGAN). The proposed model consists of two successive networks: generator and discriminator. The generator learns the mapping from the observing input (i.e., image and background), to the output (i.e., foreground mask). Then, the discriminator learns a loss function to train this mapping by comparing real foreground (i.e., ground-truth) and fake foreground (i.e., predicted output) with observing the input image and background. Evaluating the model performance with two public datasets, CDnet 2014 and BMC, shows that the proposed model outperforms the state-of-the-art methods. |