{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:46:16Z","timestamp":1761896776195,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T00:00:00Z","timestamp":1524441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.<\/jats:p>","DOI":"10.3390\/s18041292","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"1292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight"],"prefix":"10.3390","volume":"18","author":[{"given":"Siqiu","family":"Guo","sequence":"first","affiliation":[{"name":"Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, 3888 Dongnanhu Road, Changchun 130033, China"},{"name":"University of Chinese Academy of Science, 19 Yuquan Road, Beijing 100049, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, 3888 Dongnanhu Road, Changchun 130033, China"}]},{"given":"Yulong","family":"Song","sequence":"additional","affiliation":[{"name":"Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, 3888 Dongnanhu Road, Changchun 130033, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7368-5835","authenticated-orcid":false,"given":"Feng","family":"Qian","sequence":"additional","affiliation":[{"name":"Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, 3888 Dongnanhu Road, Changchun 130033, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20736","DOI":"10.3390\/s141120736","article-title":"A Target Model Construction Algorithm for Robust Real-Time Mean-Shift Tracking","volume":"14","author":"Choi","year":"2014","journal-title":"Sensors"},{"key":"ref_2","first-page":"871314","article-title":"An integrated multitarget tracking system for interacting target scenarios","volume":"8713","author":"Mao","year":"2013","journal-title":"Airborne Intell. 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