We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Computer Science


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


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.