Background subtraction based on deep convolutional neural networks features

Author:

Dou Jianfang,Qin Qin,Tu Zimei

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference40 articles.

1. Balcilar M, Karabiber F, and Sonmez A (2013) Performance analysis of Lab2000HL color space for background subtraction. IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2013

2. Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Trans Image Process 20(6):1709–1724

3. Bilodeau GA, Jodoin JP, Saunier N. (2013) Change detection in feature space using local binary similarity patterns[C]//Computer and Robot Vision (CRV), 2013 International Conference on. IEEE, 106–112

4. Bouwmans T (2011) Recent advanced statistical background modeling for foreground detection—a systematic survey. Recent Patents Comput Sci 4(3):147–176

5. Bouwmans T, Silva C, Marghes C, et al. (2016) On the Role and the Importance of Features for Background Modeling and Foreground Detection[J]. arXiv preprint arXiv:1611.09099

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