Target tracking based on hierarchical feature fusion of residual neural network
文献类型：会议论文
| 作者 | Jin, Hui1,2,3; Li, XinYang1,2 |
| 出版日期 | 2019 |
| 会议日期 | August 23, 2019 - August 25, 2019 |
| 会议地点 | Shanghai, China |
| 关键词 | Target Tracking Residual Neural Network feature fusion addition layer OPE |
| 卷号 | 11321 |
| DOI | 10.1117/12.2547560 |
| 页码 | 113211H |
| 英文摘要 | Feature expression is a crucial part of the target tracking process. The artificial feature is relatively simple and has strong real-time performance, but there is a problem of insufficient representation ability. It is prone to drift when dealing with problems such as rapid change and target occlusion. With the strong feature expression ability of deep neural network features in target detection and recognition tasks, deep neural network features are gradually used as feature extraction tools, but how to use and integrate these features is still worth studying. In this paper, the Residual Neural Network(ResNet) is the main researched object, and the influence of each layer on the target tracking performance is analyzed in detail. The feature fusion strategy of the convolutional layer and the addition layer is finally determined. We train a classifier separately for these layers. Then we search the multi-layer response maps to infer the target location in a coarse-to-fine fashion. The algorithm of this paper is verified on the OTB-50 dataset. The one-pass evalution(OPE) value can reach 0.612, which is better than the same type of algorithms. © 2019 SPIE. |
| 会议录 | Proceedings of SPIE 11321 - 2019 International Conference on Image and Video Processing, and Artificial Intelligence
 |
| 会议录出版者 | SPIE |
| 文献子类 | 会议论文 |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| WOS研究方向 | Computer Science, Artificial Intelligence ; Optics ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000511402300051 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/9633]  |
| 专题 | 光电技术研究所_自适应光学技术研究室（八室） |
| 作者单位 | 1.Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan; 610209, China; 2.Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan; 610209, China; 3.University of Chinese Academy of Sciences, Beijing; 100049, China |
| 推荐引用方式 GB/T 7714 | Jin, Hui,Li, XinYang. Target tracking based on hierarchical feature fusion of residual neural network[C]. 见:. Shanghai, China. August 23, 2019 - August 25, 2019. |
入库方式： OAI收割
来源：光电技术研究所
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