Huang S.-CLiu HChen B.-HFang ZTan T.-HSY-YEN KUO2021-09-022021-09-0220191530437Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85056173064&doi=10.1109%2fJSEN.2018.2879187&partnerID=40&md5=d936b869e3b3194ad7378192433d9fc7https://scholars.lib.ntu.edu.tw/handle/123456789/581165The automated detection of moving objects is an essential task for any intelligent transportation system. To achieve reliable and accurate motion detection in video streams acquired from either jitter or static cameras in real-world scenarios, a novel motion detection approach based on gray relational analysis is proposed in this paper, which integrates a multi-sample background generation module and a moving object detection module. As our experimental results demonstrate, the proposed approach attains superior motion detection performance compared to other state-of-the-art techniques based on qualitative and quantitative evaluations. In addition, the processing speed of the proposed approach makes it suitable for real-time applications. ? 2001-2012 IEEE.Cameras; Electrical engineering; Intelligent systems; Jitter; Media streaming; Object detection; Object recognition; Security systems; Sensors; Signal detection; Accurate motion detection; Grey relational analysis; Intelligent transportation systems; Jitter camera; Motion detection; State-of-the-art techniques; Streaming media; Surveillance sensors; Motion analysisjournal article10.1109/JSEN.2018.28791872-s2.0-85056173064