Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
Title:
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
Other Titles:
2019 IEEE International Conference on Image Processing (ICIP)
DOI:
10.1109/ICIP.2019.8803386
Publication Date:
22 September 2019
Citation:
L. Wang, T. Liu, B. Wang, J. Lin, X. Yang and G. Wang, "Learning Hierarchical Features for Visual Object Tracking With Recursive Neural Networks," 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 3088-3092.
Abstract:
Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have a relatively small number of parameters compared to other deep neural networks (e.g. Convolutional Neural Networks (CNN)) due to all basic modules in RNN share only one set of parameters. Experimental results demonstrate that our feature learning algorithm can significantly improve tracking performance on benchmark datasets.
License type:
PublisherCopyrights
Funding Info:
Description:
(C) 2019 IEEE
ISSN:
2381-8549
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