CTT: CNN Meets Transformer for Tracking
文献类型:期刊论文
作者 | Yang, Chen1,2; Zhang, Ximing2; Song, Zongxi2 |
刊名 | SENSORS |
出版日期 | 2022-05 |
卷号 | 22期号:9 |
ISSN号 | 1424-8220 |
关键词 | self-attention tracking transformer CNN |
DOI | 10.3390/s22093210 |
产权排序 | 1 |
英文摘要 | Siamese networks are one of the most popular directions in the visual object tracking based on deep learning. In Siamese networks, the feature pyramid network (FPN) and the cross-correlation complete feature fusion and the matching of features extracted from the template and search branch, respectively. However, object tracking should focus on the global and contextual dependencies. Hence, we introduce a delicate residual transformer structure which contains a self-attention mechanism called encoder-decoder into our tracker as the part of neck. Under the encoder-decoder structure, the encoder promotes the interaction between the low-level features extracted from the target and search branch by the CNN to obtain global attention information, while the decoder replaces cross-correlation to send global attention information into the head module. We add a spatial and channel attention component in the target branch, which can further improve the accuracy and robustness of our proposed model for a low price. Finally, we detailly evaluate our tracker CTT on GOT-10k, VOT2019, OTB-100, LaSOT, NfS, UAV123 and TrackingNet benchmarks, and our proposed method obtains competitive results with the state-of-the-art algorithms. |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000794657200001 |
源URL | [http://ir.opt.ac.cn/handle/181661/95888] |
专题 | 西安光学精密机械研究所_空间光学应用研究室 |
通讯作者 | Song, Zongxi |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710000, Peoples R China |
推荐引用方式 GB/T 7714 | Yang, Chen,Zhang, Ximing,Song, Zongxi. CTT: CNN Meets Transformer for Tracking[J]. SENSORS,2022,22(9). |
APA | Yang, Chen,Zhang, Ximing,&Song, Zongxi.(2022).CTT: CNN Meets Transformer for Tracking.SENSORS,22(9). |
MLA | Yang, Chen,et al."CTT: CNN Meets Transformer for Tracking".SENSORS 22.9(2022). |
入库方式: OAI收割
来源:西安光学精密机械研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。