Target tracking based on hierarchical feature fusion of residual neural network

Hui Jin; XinYang Li

doi:10.1117/12.2547560

27 November 2019 Target tracking based on hierarchical feature fusion of residual neural network

Hui Jin, XinYang Li

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211H (2019) https://doi.org/10.1117/12.2547560
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Hui Jin and XinYang Li "Target tracking based on hierarchical feature fusion of residual neural network", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211H (27 November 2019); https://doi.org/10.1117/12.2547560

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