Visual Object Tracking in RGB-D Data via Genetic Feature Learning
Visual object tracking is a fundamental component in many computer vision applications. 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 propos...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/4539410 |
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| author | Ming-xin Jiang Xian-xian Luo Tao Hai Hai-yan Wang Song Yang Ahmed N. Abdalla |
| author_facet | Ming-xin Jiang Xian-xian Luo Tao Hai Hai-yan Wang Song Yang Ahmed N. Abdalla |
| author_sort | Ming-xin Jiang |
| collection | DOAJ |
| description | Visual object tracking is a fundamental component in many computer vision applications. 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-D 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-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed. |
| format | Article |
| id | doaj-art-aebfedba454f46e6beb93d47a3b6f493 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-aebfedba454f46e6beb93d47a3b6f4932025-08-20T03:38:59ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/45394104539410Visual Object Tracking in RGB-D Data via Genetic Feature LearningMing-xin Jiang0Xian-xian Luo1Tao Hai2Hai-yan Wang3Song Yang4Ahmed N. Abdalla5Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, ChinaFaculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, ChinaComputer Science Department, Baoji University of Arts and Sciences, Shanxi 721031, ChinaJiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, ChinaJiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, ChinaJiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, ChinaVisual object tracking is a fundamental component in many computer vision applications. 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-D 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-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.http://dx.doi.org/10.1155/2019/4539410 |
| spellingShingle | Ming-xin Jiang Xian-xian Luo Tao Hai Hai-yan Wang Song Yang Ahmed N. Abdalla Visual Object Tracking in RGB-D Data via Genetic Feature Learning Complexity |
| title | Visual Object Tracking in RGB-D Data via Genetic Feature Learning |
| title_full | Visual Object Tracking in RGB-D Data via Genetic Feature Learning |
| title_fullStr | Visual Object Tracking in RGB-D Data via Genetic Feature Learning |
| title_full_unstemmed | Visual Object Tracking in RGB-D Data via Genetic Feature Learning |
| title_short | Visual Object Tracking in RGB-D Data via Genetic Feature Learning |
| title_sort | visual object tracking in rgb d data via genetic feature learning |
| url | http://dx.doi.org/10.1155/2019/4539410 |
| work_keys_str_mv | AT mingxinjiang visualobjecttrackinginrgbddataviageneticfeaturelearning AT xianxianluo visualobjecttrackinginrgbddataviageneticfeaturelearning AT taohai visualobjecttrackinginrgbddataviageneticfeaturelearning AT haiyanwang visualobjecttrackinginrgbddataviageneticfeaturelearning AT songyang visualobjecttrackinginrgbddataviageneticfeaturelearning AT ahmednabdalla visualobjecttrackinginrgbddataviageneticfeaturelearning |