Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm
With the aging of the population and the shortage of labor, the demand for service robots is increasing. As the key performance of visual tracking, it still has the problems of low tracking accuracy and poor real-time performance. Therefore, this paper studies the use of the You Only Look Once serie...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10759675/ |
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| _version_ | 1846128578832367616 |
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| author | Lijuan Xu Dalong Liu Huanjian Ma |
| author_facet | Lijuan Xu Dalong Liu Huanjian Ma |
| author_sort | Lijuan Xu |
| collection | DOAJ |
| description | With the aging of the population and the shortage of labor, the demand for service robots is increasing. As the key performance of visual tracking, it still has the problems of low tracking accuracy and poor real-time performance. Therefore, this paper studies the use of the You Only Look Once series algorithm and the use of the regression network general target tracking algorithm to improve the detection and tracker part of the track-learning-detection algorithm. At the same time, three strategies for the attention mechanism, multi-level feature fusion, and regional overlap loss function are introduced to improve the visual tracking model. Therefore, a new robot visual tracking model is constructed. The experimental results showed that the average score of the research model in the success rate index was 0.68, which was significantly higher than other models. In the precision graph index score, the average score of the research model was 0.79 and the curves were all in the outermost circle. In practical application, the Expected Average Overlap rate index value of the research model was 0.4027 and the average mean Average Precision value was 91.07%. Compared with the other four models, its comprehensive performance was significantly better. The research model can maintain high robustness under different challenges and the visual tracking accuracy and real-time performance are better. It can provide more effective technical support for the service field of robots and promote the development of the robot field. |
| format | Article |
| id | doaj-art-f5514e4a50a94df99592dca5d5b4e12e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f5514e4a50a94df99592dca5d5b4e12e2024-12-11T00:04:39ZengIEEEIEEE Access2169-35362024-01-011217998117999610.1109/ACCESS.2024.350367410759675Robot Visual Tracking Model Based on Improved GOTURN-LD AlgorithmLijuan Xu0Dalong Liu1https://orcid.org/0009-0003-9895-3554Huanjian Ma2School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, ChinaSchool of Intelligent Engineering, Guangzhou Huashang Vocational College, Guangzhou, ChinaSchool of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, ChinaWith the aging of the population and the shortage of labor, the demand for service robots is increasing. As the key performance of visual tracking, it still has the problems of low tracking accuracy and poor real-time performance. Therefore, this paper studies the use of the You Only Look Once series algorithm and the use of the regression network general target tracking algorithm to improve the detection and tracker part of the track-learning-detection algorithm. At the same time, three strategies for the attention mechanism, multi-level feature fusion, and regional overlap loss function are introduced to improve the visual tracking model. Therefore, a new robot visual tracking model is constructed. The experimental results showed that the average score of the research model in the success rate index was 0.68, which was significantly higher than other models. In the precision graph index score, the average score of the research model was 0.79 and the curves were all in the outermost circle. In practical application, the Expected Average Overlap rate index value of the research model was 0.4027 and the average mean Average Precision value was 91.07%. Compared with the other four models, its comprehensive performance was significantly better. The research model can maintain high robustness under different challenges and the visual tracking accuracy and real-time performance are better. It can provide more effective technical support for the service field of robots and promote the development of the robot field.https://ieeexplore.ieee.org/document/10759675/GOTURN algorithmTLD algorithmrobotsvisual trackingreal-time performanceimprovement strategy |
| spellingShingle | Lijuan Xu Dalong Liu Huanjian Ma Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm IEEE Access GOTURN algorithm TLD algorithm robots visual tracking real-time performance improvement strategy |
| title | Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm |
| title_full | Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm |
| title_fullStr | Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm |
| title_full_unstemmed | Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm |
| title_short | Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm |
| title_sort | robot visual tracking model based on improved goturn ld algorithm |
| topic | GOTURN algorithm TLD algorithm robots visual tracking real-time performance improvement strategy |
| url | https://ieeexplore.ieee.org/document/10759675/ |
| work_keys_str_mv | AT lijuanxu robotvisualtrackingmodelbasedonimprovedgoturnldalgorithm AT dalongliu robotvisualtrackingmodelbasedonimprovedgoturnldalgorithm AT huanjianma robotvisualtrackingmodelbasedonimprovedgoturnldalgorithm |