Real-Time Semantic Background Subtraction
This work addresses the need for real-time background subtraction in practical applications, offering an incremental improvement by adapting SBS for speed constraints.
The paper tackles the problem of slow semantic background subtraction (SBS) by introducing RT-SBS, a real-time algorithm that combines a fast background subtraction method with high-quality semantic information, achieving state-of-the-art performance in real-time and competing with non-real-time methods.
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.