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Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB\u2010D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB\u2010D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.<\/jats:p>","DOI":"10.1155\/2019\/4539410","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T23:34:47Z","timestamp":1556840087000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Visual Object Tracking in RGB\u2010D Data via Genetic Feature Learning"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0766-5841","authenticated-orcid":false,"given":"Ming-xin","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5609-557X","authenticated-orcid":false,"given":"Xian-xian","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Hai","sequence":"additional","affiliation":[]},{"given":"Hai-yan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Song","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Ahmed N.","family":"Abdalla","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,5,2]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.07.005"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.242"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2706978"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2017.02.002"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2669880"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2626275"},{"key":"e_1_2_8_7_2","first-page":"1","article-title":"Online depth image-based object tracking with sparse representation and object detection","volume":"45","author":"Zheng W.-L.","year":"2016","journal-title":"Neural Processing Letters"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"NingA. 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