A Ranking based Attention Approach for Visual Tracking
Peng, S.; Kamata, S.; Breckon, T.P.
Professor Toby Breckon email@example.com
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.
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||Nov 12, 2019|
|Book Title||2019 IEEE International Conference on Image Processing (ICIP) ; proceedings.|
Accepted Conference Proceeding
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