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Department光电探测与信号处理研究室(五室)
Multi-feature fusion siamese network for real-time object tracking
Zhou, Lijun1; Li, Hongyun1; Zhang, Jianlin2
Source PublicationACM International Conference Proceeding Series
Pages478-481
2018-12-08
Language英语
DOI10.1145/3297156.3297259
Indexed ByEi
EI Accession Number20191106628876
SubtypeC
AbstractIn 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.
KeywordBenchmarking Multilayer neural networks Multimedia systems Semantics
EI KeywordsBenchmarking ; Multilayer neural networks ; Multimedia systems ; Semantics
Conference Name2nd International Conference on Computer Science and Artificial Intelligence, CSAI 2018
Conference DateDecember 8, 2018 - December 10, 2018
Conference PlaceShenzhen, China
EI Classification Number723.5 Computer Applications
Citation statistics
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/9122
Collection光电探测与信号处理研究室(五室)
Affiliation1.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
Recommended Citation
GB/T 7714
Zhou, Lijun,Li, Hongyun,Zhang, Jianlin. Multi-feature fusion siamese network for real-time object tracking[C],2018:478-481.
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