Reference : Real-Time Semantic Background Subtraction
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/247775
Real-Time Semantic Background Subtraction
English
Cioppa, Anthony mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Van Droogenbroeck, Marc mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Braham, Marc mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Oct-2020
Proceedings of the IEEE International Conference on Image Processing (ICIP)
3214-3218
Yes
No
International
IEEE International Conference on Image Processing (ICIP)
from 25-10-2020 to 28-10-2020
IEEE
Abu Dhabi
United Arab Emirates
[en] Background subtraction ; Real time ; Change detection ; Semantic segmentation ; SBS ; ViBe ; CDNet 2014 ; CPU ; GPU
[en] Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that we provide python CPU and GPU implementations of RT-SBS at https://github.com/cioppaanthony/rt-sbs
Montefiore Institute of Electrical Engineering and Computer Science - Montefiore Institute ; Telim
Researchers ; Professionals
http://hdl.handle.net/2268/247775
10.1109/ICIP40778.2020.9190838
https://github.com/cioppaanthony/rt-sbs
https://arxiv.org/abs/2002.04993
See https://github.com/cioppaanthony/rt-sbs for Python code (CPU and GPU implementations are available)

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