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The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.<\/jats:p>","DOI":"10.1155\/2020\/8839725","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T02:36:32Z","timestamp":1608258992000},"page":"1-13","source":"Crossref","is-referenced-by-count":1,"title":["Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7969-8373","authenticated-orcid":true,"given":"Ngo Duong","family":"Ha","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, University of Science, Vietnam National University-Ho Chi Minh City, Ho Chi Minh, Vietnam"},{"name":"Information Technology Faculty, Ho Chi Minh City University of Food Industry, Ho Chi Minh, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikuko","family":"Shimizu","sequence":"additional","affiliation":[{"name":"Tokyo University of Agriculture and Technology, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4847-4366","authenticated-orcid":true,"given":"Pham The","family":"Bao","sequence":"additional","affiliation":[{"name":"Information Science Faculty, Sai Gon University, Ho Chi Minh, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-14364-4_5","article-title":"Spatio-temporal level-set based cell segmentation in time-lapse image sequences","volume-title":"International Symposium on Visual Computing","author":"F. 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