Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning
Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Se...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4695890 |
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| _version_ | 1849696316138979328 |
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| author | Ming-xin Jiang Chao Deng Zhi-geng Pan Lan-fang Wang Xing Sun |
| author_facet | Ming-xin Jiang Chao Deng Zhi-geng Pan Lan-fang Wang Xing Sun |
| author_sort | Ming-xin Jiang |
| collection | DOAJ |
| description | Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened. |
| format | Article |
| id | doaj-art-cc01435e4e9046a9838d405fa60e7e61 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-cc01435e4e9046a9838d405fa60e7e612025-08-20T03:19:29ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/46958904695890Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement LearningMing-xin Jiang0Chao Deng1Zhi-geng Pan2Lan-fang Wang3Xing Sun4Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huaian 223003, ChinaSchool of Physics & Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaInstitute of Industrial VR, Foshan University, Foshan 528000, ChinaFaculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huaian 223003, ChinaMultiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened.http://dx.doi.org/10.1155/2018/4695890 |
| spellingShingle | Ming-xin Jiang Chao Deng Zhi-geng Pan Lan-fang Wang Xing Sun Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning Complexity |
| title | Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning |
| title_full | Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning |
| title_fullStr | Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning |
| title_full_unstemmed | Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning |
| title_short | Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning |
| title_sort | multiobject tracking in videos based on lstm and deep reinforcement learning |
| url | http://dx.doi.org/10.1155/2018/4695890 |
| work_keys_str_mv | AT mingxinjiang multiobjecttrackinginvideosbasedonlstmanddeepreinforcementlearning AT chaodeng multiobjecttrackinginvideosbasedonlstmanddeepreinforcementlearning AT zhigengpan multiobjecttrackinginvideosbasedonlstmanddeepreinforcementlearning AT lanfangwang multiobjecttrackinginvideosbasedonlstmanddeepreinforcementlearning AT xingsun multiobjecttrackinginvideosbasedonlstmanddeepreinforcementlearning |