Co-occurrence Background Model with Superpixels for Robust Background Initialization
This addresses background initialization for video surveillance and inpainting, but it appears incremental as it builds on existing modeling and segmentation techniques.
The paper tackled the problem of robust background initialization in video processing by developing a co-occurrence background model with superpixel segmentation, achieving validated performance on the challenging SBMnet benchmark dataset.
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.