University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking

Zhu, Xue-Feng, Wu, Xiao-Jun, Xu, Tianyang, Feng, Zhenhua and Kittler, Josef (2020) Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking IEEE Transactions on Circuits and Systems for Video Technology.

[img]
Preview
Text
CSLDCF_TCSVT_2020.pdf - Accepted version Manuscript

Download (4MB) | Preview

Abstract

In recent years, discriminative correlation filter (DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trained with powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation between successive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combined with the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with a number of state-of-the-art tracking algorithms.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Zhu, Xue-Feng
Wu, Xiao-Jun
Xu, Tianyangtx0002@surrey.ac.uk
Feng, Zhenhuaz.feng@surrey.ac.uk
Kittler, JosefJ.Kittler@surrey.ac.uk
Date : 9 March 2020
Funders : EPSRC
DOI : 10.1109/TCSVT.2020.2979480
Grant Title : EPSRC Grant - FACER2VM
Uncontrolled Keywords : Visual object tracking, discriminative correlation filter, feature representation, collaborative representation
Depositing User : James Marshall
Date Deposited : 11 Mar 2020 14:11
Last Modified : 11 Mar 2020 14:16
URI: http://epubs.surrey.ac.uk/id/eprint/853907

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800