Comprehensive survey on target tracking based on Siamese network
In recent years,the target tracking algorithm based on Siamese network has attracted much attention because it can achieve a good balance between tracking accuracy and tracking efficiency.Through the intensive study of the literature of target tracking algorithm based on Siamese network,the existing...
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| Format: | Article |
| Language: | zho |
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Hebei University of Science and Technology
2022-02-01
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| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202201004&flag=1&journal_ |
| _version_ | 1797792722926436352 |
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| author | Ming HAN Jingqin WANG Jingtao WANG Junying MENG Jiaomin LIU |
| author_facet | Ming HAN Jingqin WANG Jingtao WANG Junying MENG Jiaomin LIU |
| author_sort | Ming HAN |
| collection | DOAJ |
| description | In recent years,the target tracking algorithm based on Siamese network has attracted much attention because it can achieve a good balance between tracking accuracy and tracking efficiency.Through the intensive study of the literature of target tracking algorithm based on Siamese network,the existing target tracking algorithm based on Siamese network was comprehensively summarized.Firstly,the basic framework of target tracking was introduced based on Siamese network,and the optimized backbone network in Siamese network and its target feature extraction were analyzed.Secondly,the classification and regression tasks in the process of target tracking were discussed,which were divided into two categories of anchor frame and anchor-free frame.The advantages and disadvantages of the algorithm as well as the target tracking performance were analyzed through experimental comparison.Finally,the focus of future research is proposed as following:1) Explore the training of background information,realize the dissemination of background information in the scene,and make full use of background information to achieve target positioning.2) In the process of target tracking,the target feature information is enriched and the target tracking frame is changed adaptively.3) Research from the global information transmission between frames to the target local information transmission provides support for the accurate target positioning and tracking.[HQ] |
| first_indexed | 2024-03-13T02:37:55Z |
| format | Article |
| id | doaj.art-f768fb4b34d44d92ad31dc0459174242 |
| institution | Directory Open Access Journal |
| issn | 1008-1542 |
| language | zho |
| last_indexed | 2024-03-13T02:37:55Z |
| publishDate | 2022-02-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj.art-f768fb4b34d44d92ad31dc04591742422023-06-29T01:14:28ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422022-02-014312741b202201004Comprehensive survey on target tracking based on Siamese networkMing HAN0Jingqin WANG1Jingtao WANG2Junying MENG3Jiaomin LIU4School of Computer Science and Engineering,Shijiazhuang University,Shijiazhuang,Hebei 050035,ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,ChinaSchool of Computer Science and Engineering,Shijiazhuang University,Shijiazhuang,Hebei 050035,ChinaSchool of Computer Science and Engineering,Shijiazhuang University,Shijiazhuang,Hebei 050035,ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,ChinaIn recent years,the target tracking algorithm based on Siamese network has attracted much attention because it can achieve a good balance between tracking accuracy and tracking efficiency.Through the intensive study of the literature of target tracking algorithm based on Siamese network,the existing target tracking algorithm based on Siamese network was comprehensively summarized.Firstly,the basic framework of target tracking was introduced based on Siamese network,and the optimized backbone network in Siamese network and its target feature extraction were analyzed.Secondly,the classification and regression tasks in the process of target tracking were discussed,which were divided into two categories of anchor frame and anchor-free frame.The advantages and disadvantages of the algorithm as well as the target tracking performance were analyzed through experimental comparison.Finally,the focus of future research is proposed as following:1) Explore the training of background information,realize the dissemination of background information in the scene,and make full use of background information to achieve target positioning.2) In the process of target tracking,the target feature information is enriched and the target tracking frame is changed adaptively.3) Research from the global information transmission between frames to the target local information transmission provides support for the accurate target positioning and tracking.[HQ]http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202201004&flag=1&journal_computer image processing; target tracking; siamese network; deep learning; feature extraction |
| spellingShingle | Ming HAN Jingqin WANG Jingtao WANG Junying MENG Jiaomin LIU Comprehensive survey on target tracking based on Siamese network Journal of Hebei University of Science and Technology computer image processing; target tracking; siamese network; deep learning; feature extraction |
| title | Comprehensive survey on target tracking based on Siamese network |
| title_full | Comprehensive survey on target tracking based on Siamese network |
| title_fullStr | Comprehensive survey on target tracking based on Siamese network |
| title_full_unstemmed | Comprehensive survey on target tracking based on Siamese network |
| title_short | Comprehensive survey on target tracking based on Siamese network |
| title_sort | comprehensive survey on target tracking based on siamese network |
| topic | computer image processing; target tracking; siamese network; deep learning; feature extraction |
| url | http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202201004&flag=1&journal_ |
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