Generative Target Tracking Method with Improved Generative Adversarial Network
Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, u...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | IET Circuits, Devices and Systems |
| Online Access: | http://dx.doi.org/10.1049/2023/6620581 |
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| _version_ | 1849396684758450176 |
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| author | Yongping Yang Hongshun Chen |
| author_facet | Yongping Yang Hongshun Chen |
| author_sort | Yongping Yang |
| collection | DOAJ |
| description | Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, using the improved you only look once multitarget association algorithm to classify and detect the position of the target to be detected in the current frame, constructing a feature extraction model using generative adversarial networks (GANs) to learn the main features and subtle features of the target, and then using GANs to generate the motion trajectories of multiple targets, finally fuzing the motion and appearance information of the target to obtain the optimal match. The optimal matching of the tracked targets is obtained. The experimental results under OTB2015 and IVOT2018 datasets demonstrate that the proposed multitarget tracking algorithm has high accuracy and robustness, with 65% less jumps and 0.25% more accuracy than the current algorithms with minimal identity exchange and jumps. |
| format | Article |
| id | doaj-art-afe8aec5719b4c3887974c9e4e13300b |
| institution | Kabale University |
| issn | 1751-8598 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Circuits, Devices and Systems |
| spelling | doaj-art-afe8aec5719b4c3887974c9e4e13300b2025-08-20T03:39:15ZengWileyIET Circuits, Devices and Systems1751-85982023-01-01202310.1049/2023/6620581Generative Target Tracking Method with Improved Generative Adversarial NetworkYongping Yang0Hongshun Chen1School of Information TechnologySchool of Information TechnologyMultitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, using the improved you only look once multitarget association algorithm to classify and detect the position of the target to be detected in the current frame, constructing a feature extraction model using generative adversarial networks (GANs) to learn the main features and subtle features of the target, and then using GANs to generate the motion trajectories of multiple targets, finally fuzing the motion and appearance information of the target to obtain the optimal match. The optimal matching of the tracked targets is obtained. The experimental results under OTB2015 and IVOT2018 datasets demonstrate that the proposed multitarget tracking algorithm has high accuracy and robustness, with 65% less jumps and 0.25% more accuracy than the current algorithms with minimal identity exchange and jumps.http://dx.doi.org/10.1049/2023/6620581 |
| spellingShingle | Yongping Yang Hongshun Chen Generative Target Tracking Method with Improved Generative Adversarial Network IET Circuits, Devices and Systems |
| title | Generative Target Tracking Method with Improved Generative Adversarial Network |
| title_full | Generative Target Tracking Method with Improved Generative Adversarial Network |
| title_fullStr | Generative Target Tracking Method with Improved Generative Adversarial Network |
| title_full_unstemmed | Generative Target Tracking Method with Improved Generative Adversarial Network |
| title_short | Generative Target Tracking Method with Improved Generative Adversarial Network |
| title_sort | generative target tracking method with improved generative adversarial network |
| url | http://dx.doi.org/10.1049/2023/6620581 |
| work_keys_str_mv | AT yongpingyang generativetargettrackingmethodwithimprovedgenerativeadversarialnetwork AT hongshunchen generativetargettrackingmethodwithimprovedgenerativeadversarialnetwork |