Su, S.-T.S.-T.SuYUNG-YAW CHEN2020-06-112020-06-112008https://scholars.lib.ntu.edu.tw/handle/123456789/501101https://www.scopus.com/inward/record.uri?eid=2-s2.0-67549130636&doi=10.1109%2fDICTA.2008.15&partnerID=40&md5=098b81901ff98283c4a796d532801b84Moving object segmentation using Improved Running Gaussian Average Background Model (IRGABM) is proposed in this paper. Background subtraction for a relatively static background is a popular method for moving object segmentation in image sequences. However, there are some problems for the background subtraction method, such as the varying luminance effect, the background updating problem, and the noise effect. IRGABM has the advantages of fast computational speed and low memory requirement. Our study also shows its improvements on the above-mentioned problems. For the purpose of moving object segmentation, background updating time, auto-thresholding and shadow detection are also discussed in this paper. © 2008 IEEE.Background model; Background subtraction; Background subtraction method; Computational speed; Gaussian; Image sequence; Low memory; Moving object segmentation; Noise effects; Shadow detections; Static background; Thresholding; Image segmentationconference paper10.1109/DICTA.2008.152-s2.0-67549130636