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The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community.<\/jats:p>","DOI":"10.1007\/s11042-020-09096-x","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T16:23:11Z","timestamp":1592929391000},"page":"24537-24551","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Person Re-identification in Videos by Analyzing Spatio-temporal Tubes"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0706-2565","authenticated-orcid":false,"given":"Arif Ahmed","family":"Sekh","sequence":"first","affiliation":[]},{"given":"Debi Prosad","family":"Dogra","sequence":"additional","affiliation":[]},{"given":"Heeseung","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Seungho","family":"Chae","sequence":"additional","affiliation":[]},{"given":"Ig-Jae","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,23]]},"reference":[{"key":"9096_CR1","doi-asserted-by":"crossref","unstructured":"Barman A, Shah SK (2017) Shape: A novel graph theoretic algorithm for making consensus-based decisions in person re-identification systems. 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Informed consent: Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}