Multi-feature fusion siamese network for real-time object tracking
文献类型：会议论文
| 作者 | Zhou, Lijun1; Li, Hongyun1; Zhang, Jianlin2 |
| 出版日期 | 2018-12-08 |
| 会议日期 | December 8, 2018 - December 10, 2018 |
| 会议地点 | Shenzhen, China |
| 关键词 | Benchmarking Multilayer neural networks Multimedia systems Semantics |
| DOI | 10.1145/3297156.3297259 |
| 页码 | 478-481 |
| 英文摘要 | In the multilayer neural network, the features of the low-level layers are of high resolution, which is suitable for positioning the object, while the features of the high-level layers are of rich semantics features which are suitable for the classifying the object. In order to utilize the advantage of high-level features and low-level features, we introduce a densely connected network called DSiamFc(Densely Connected Siamese Networks). Not only the low-level features and high-level features are fully integrated, but also this connection method can provide better parameter adjustment for the whole network during off-line training for the end-to-end object tracking network. The effectiveness of our proposed network is demonstrated by analyzing the backpropagation of gradient flow. Our algorithm is able to achieve real-time, and in the OTB-2013/50/100 benchmark, our algorithm has the best performance compared to other state-of-the-art real-time object tracking algorithms. © 2018 Association for Computing Machinery. |
| 会议录 | ACM International Conference Proceeding Series
 |
| 文献子类 | C |
| 语种 | 英语 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/9122]  |
| 专题 | 光电技术研究所_光电探测与信号处理研究室（五室） |
| 作者单位 | 1.Institute of Optics and Electronics, Chinese Academy of Sciences, University of Chinese, Academy of Sciences, No.1, Optoelectronic Avenue, Wenxing Town, Shuangliu District, Chengdu, China; 2.Institute of Optics and Electronics, Chinese Academy of Sciences, No.1, Optoelectronic Avenue, Wenxing Town, Shuangliu District, Chengdu, China |
| 推荐引用方式 GB/T 7714 | Zhou, Lijun,Li, Hongyun,Zhang, Jianlin. Multi-feature fusion siamese network for real-time object tracking[C]. 见:. Shenzhen, China. December 8, 2018 - December 10, 2018. |
入库方式： OAI收割
来源：光电技术研究所
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