A Ranking Based Attention Approach for Visual Tracking

Shenhui Peng, Sei Ichiro Kamata, Toby P. Breckon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages3073-3077
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - 2019 Sep
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 2019 Sep 222019 Sep 25

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period19/9/2219/9/25

Keywords

  • ConvLSTM
  • Convolutional tracker
  • Ranking based attention
  • Visual tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint Dive into the research topics of 'A Ranking Based Attention Approach for Visual Tracking'. Together they form a unique fingerprint.

  • Cite this

    Peng, S., Kamata, S. I., & Breckon, T. P. (2019). A Ranking Based Attention Approach for Visual Tracking. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (pp. 3073-3077). [8803358] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8803358