2018/04/19 by Murari Mandal, Prafulla Saxena, Mandal, Murari +5
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote-Sensing Image Classification #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1804.07008
Accepted in ICPR-2018
arxiv created 2018/04/19 · openalex publication_date 2018/04/19 · arxiv updated 2018/04/20 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches.