Multi-layer features template update object tracking algorithm based on SiamFC++
Abstract SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13640-023-00616-x |
| _version_ | 1797363021535772672 |
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| author | Xiaofeng Lu Xuan Wang Zhengyang Wang Xinhong Hei |
| author_facet | Xiaofeng Lu Xuan Wang Zhengyang Wang Xinhong Hei |
| author_sort | Xiaofeng Lu |
| collection | DOAJ |
| description | Abstract SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this, the present paper proposes an object tracking algorithm based on SiamFC++. The algorithm uses the multi-layer features of the Siamese network to update template. First, FPN is used to extract feature maps from different layers of Backbone for classification branch and regression branch. Second, 3D convolution is used to update the tracking template of the object tracking algorithm. Next, a template update judgment condition is proposed based on mutual information. Finally, AlexNet is used as the backbone and GOT-10K as training set. Compared with SiamFC++, our algorithm obtains improved results on OTB100, VOT2016, VOT2018 and GOT-10k data sets, and the tracking process is real time. |
| first_indexed | 2024-03-08T16:14:49Z |
| format | Article |
| id | doaj.art-e71bd2f121be46e5a79c0b98f6fe22af |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-03-08T16:14:49Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-e71bd2f121be46e5a79c0b98f6fe22af2024-01-07T12:39:43ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812024-01-012024111710.1186/s13640-023-00616-xMulti-layer features template update object tracking algorithm based on SiamFC++Xiaofeng Lu0Xuan Wang1Zhengyang Wang2Xinhong Hei3Department of Computer Science and Technology, Xi’an University of TechnologyDepartment of Computer Science and Technology, Xi’an University of TechnologyDepartment of Computer Science and Technology, Xi’an University of TechnologyDepartment of Computer Science and Technology, Xi’an University of TechnologyAbstract SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this, the present paper proposes an object tracking algorithm based on SiamFC++. The algorithm uses the multi-layer features of the Siamese network to update template. First, FPN is used to extract feature maps from different layers of Backbone for classification branch and regression branch. Second, 3D convolution is used to update the tracking template of the object tracking algorithm. Next, a template update judgment condition is proposed based on mutual information. Finally, AlexNet is used as the backbone and GOT-10K as training set. Compared with SiamFC++, our algorithm obtains improved results on OTB100, VOT2016, VOT2018 and GOT-10k data sets, and the tracking process is real time.https://doi.org/10.1186/s13640-023-00616-xObject trackingFully convolutional Siamese networksTemplate updateMutual informationFPN |
| spellingShingle | Xiaofeng Lu Xuan Wang Zhengyang Wang Xinhong Hei Multi-layer features template update object tracking algorithm based on SiamFC++ EURASIP Journal on Image and Video Processing Object tracking Fully convolutional Siamese networks Template update Mutual information FPN |
| title | Multi-layer features template update object tracking algorithm based on SiamFC++ |
| title_full | Multi-layer features template update object tracking algorithm based on SiamFC++ |
| title_fullStr | Multi-layer features template update object tracking algorithm based on SiamFC++ |
| title_full_unstemmed | Multi-layer features template update object tracking algorithm based on SiamFC++ |
| title_short | Multi-layer features template update object tracking algorithm based on SiamFC++ |
| title_sort | multi layer features template update object tracking algorithm based on siamfc |
| topic | Object tracking Fully convolutional Siamese networks Template update Mutual information FPN |
| url | https://doi.org/10.1186/s13640-023-00616-x |
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