论文标题
实时语义背景减法
Real-Time Semantic Background Subtraction
论文作者
论文摘要
语义背景减法SB已被证明可以通过将其与语义分割网络得出的语义信息相结合，从而提高了大多数背景减法算法的性能。但是，SBS需要对所有框架的高质量语义分割掩码，这些框架的计算缓慢。此外，大多数最新的背景减法算法不是实时的，这使得它们不适合现实世界应用。在本文中，我们提出了一种新颖的背景减法算法，称为实时语义背景减法（表示为RT-SBS），该算法将SBS扩展到实时约束应用程序的同时，同时保持相似的性能。 RT-SB有效地将实时背景减法算法与高质量的语义信息结合在一起，可以以较慢的步伐提供，每个像素独立于速度。我们表明，RT-SB和Vibe为实时背景减法算法设定了新的最新技术，甚至与非实时最新的最新最先进的算法竞争。请注意，我们在https://github.com/cioppaanthony/rt-sbs上提供RT-SBS的Python CPU和GPU实现。
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.
