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Durham University

Computer Science

Profile

Publication details for Professor Toby Breckon

Peng, S., Kamata, S. & Breckon, T.P. (2019), A Ranking based Attention Approach for Visual Tracking, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 3073-3077.

Author(s) from Durham

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.