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Target tracking based on hierarchical feature fusion of residual neural network
Author(s): Hui Jin; XinYang Li
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Paper Abstract

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.

Paper Details

Date Published: 27 November 2019
PDF: 8 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211H (27 November 2019); doi: 10.1117/12.2547560
Show Author Affiliations
Hui Jin, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
XinYang Li, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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