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A Ranking based Attention Approach for Visual Tracking

Peng, S.; Kamata, S.; Breckon, T.P.

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Authors

S. Peng

S. Kamata



Abstract

Correlation filters (CF) combined with pre-trained convolutional neural network (CNN) feature extractors have shown an admirable accuracy and speed in visual object tracking. However, existing CNN-CF based methods still suffer from the background interference and boundary effects, even when a cosine window is introduced. This paper proposes a ranking based or guided attention approach which can reduce background interference with only forward propagation. This ranking stores several convolution kernels and scores them. Subsequently, a convolutional Long Short Time Memory network (ConvLSTM) is used to update this ranking, which makes it more robust to the variation and occlusion. Moreover, a part-based multi-channel convolutional tracker is proposed to obtain the final response map. Our extensive experiments on established benchmark datasets show comparable performance against contemporary tracking approaches.

Citation

Peng, S., Kamata, S., & Breckon, T. (2019). A Ranking based Attention Approach for Visual Tracking. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (3073-3077). https://doi.org/10.1109/icip.2019.8803358

Conference Name 26th IEEE International Conference on Image Processing (ICIP)
Conference Location Taipei, Taiwan
Start Date Sep 22, 2019
End Date Sep 25, 2019
Acceptance Date Apr 30, 2019
Publication Date Sep 1, 2019
Deposit Date Jun 4, 2019
Publicly Available Date Mar 29, 2024
Pages 3073-3077
Series ISSN 2381-8549
Book Title 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings.
DOI https://doi.org/10.1109/icip.2019.8803358

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