{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T16:05:47Z","timestamp":1774195547161,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T00:00:00Z","timestamp":1581292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian  research council - Linkage project","award":["LP130100521"],"award-info":[{"award-number":["LP130100521"]}]},{"name":"Australian research council - Discovery projects","award":["DP130104404, DP160100662"],"award-info":[{"award-number":["DP130104404, DP160100662"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of d\u00e9j\u00e0 vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection\/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.<\/jats:p>","DOI":"10.3390\/s20030929","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T09:25:21Z","timestamp":1581413121000},"page":"929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["On-Line Visual Tracking with Occlusion Handling"],"prefix":"10.3390","volume":"20","author":[{"given":"Tharindu","family":"Rathnayake","sequence":"first","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4800-6554","authenticated-orcid":false,"given":"Amirali","family":"Khodadadian Gostar","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9525-1467","authenticated-orcid":false,"given":"Reza","family":"Hoseinnezhad","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8909-5728","authenticated-orcid":false,"given":"Ruwan","family":"Tennakoon","sequence":"additional","affiliation":[{"name":"School of Science, RMIT University, Melbourne VIC 3000, Australia"}]},{"given":"Alireza","family":"Bab-Hadiashar","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rathnayake, T., Hoseinnezhad, R., Tennakoon, R., and Bab-Hadiashar, A. 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