论文标题
与超级像素的共发生背景模型可靠的背景初始化
Co-occurrence Background Model with Superpixels for Robust Background Initialization
论文作者
论文摘要
背景初始化是视频处理的许多高级应用中的重要一步，从视频监视到视频介入。 We first introduce a novel co-occurrence background modeling method called as Co-occurrence Pixel-Block Pairs(CPB)to generate a reliable initial background model,and the superpixel segmentation is utilized to further acquire the spatial texture Information of foreground and background.Then,the initial background can be determined by combining the foreground extraction results with the superpixel segmentation information.Experimental results obtained from the dataset of the具有挑战性的基准（SBMNET）在各种挑战下验证其性能。
Background initialization is an important step in many high-level applications of video processing,ranging from video surveillance to video inpainting.However,this process is often affected by practical challenges such as illumination changes,background motion,camera jitter and intermittent movement,etc.In this paper,we develop a co-occurrence background model with superpixel segmentation for robust background initialization. We first introduce a novel co-occurrence background modeling method called as Co-occurrence Pixel-Block Pairs(CPB)to generate a reliable initial background model,and the superpixel segmentation is utilized to further acquire the spatial texture Information of foreground and background.Then,the initial background can be determined by combining the foreground extraction results with the superpixel segmentation information.Experimental results obtained from the dataset of the challenging benchmark(SBMnet)validate it's performance under various challenges.
