Open AccessHierarchical Convolutional Features Fusion for Visual Tracking
Author(s) -
Fan Zhang,
Shuo Chang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012134
Subject(s) - discriminative model , convolutional neural network , computer science , pattern recognition (psychology) , artificial intelligence , feature (linguistics) , benchmark (surveying) , encode , convolutional code , computer vision , algorithm , decoding methods , philosophy , linguistics , biochemistry , chemistry , geodesy , gene , geography
Hierarchical convolutional features have different impact on the tracking performance, as the higher convolutional layers encode the semantic information of targets and earlier convolutional layers are more precise to localize targets. In this paper, we propose a novel scheme for hierarchical convolutional features fusion for visual tracking. In the proposed scheme, hierarchical convolutional features are first concatenated to form the cascading feature at the feature level, and then a convolutional layer is added to reduce the feature dimension. Discriminative correlation filter (DCF) is finally utilized to obtain the target location, which is treated as a differentiable layer in the neural network. The experimental results demonstrate that our proposed scheme achieves superior performances on the visual tracking benchmark.