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Real-time semantic background subtraction

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Citation Author(s):
Anthony Cioppa, Marc Braham, Marc Van Droogenbroeck
Submitted by:
Anthony Cioppa
Last updated:
3 November 2020 - 3:12am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Anthony Cioppa
Paper Code:
1545
Categories:
Keywords:

Abstract 

Abstract: 

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.

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