2014/05/24 by Dong Liang, Liang, Dong, Shun’ichi Kaneko +2
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Analysis and Summarization #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1405.6275
arxiv created 2014/05/24 · openalex publication_date 2014/05/24 · arxiv updated 2014/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Change detection plays an important role in most video-based applications. The first stage is to build appropriate background model, which is now becoming increasingly complex as more sophisticated statistical approaches are introduced to cover challenging situations and provide reliable detection. This paper reports a simple and intuitive statistical model based on deeper learning spatial correlation among pixels: For each observed pixel, we select a group of supporting pixels with high correlation, and then use a single Gaussian to model the intensity deviations between the observed pixel and the supporting ones. In addition, a multi-channel model updating is integrated on-line and a temporal intensity constraint for each pixel is defined. Although this method is mainly designed for coping with sudden illumination changes, experimental results using all the video sequences provided on changedetection.net validate it is comparable with other recent methods under various situations.