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Learning Adaptive Discriminative Correlation Filters via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Object Tracking

Xu, Tianyang, Feng, Zhen-hua, Wu, Xiao-Jun and Kittler, Josef (2019) Learning Adaptive Discriminative Correlation Filters via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Object Tracking IEEE Transactions on Image Processing.

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Abstract

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xu, Tianyangtianyang.xu@surrey.ac.uk
Feng, Zhen-huaz.feng@surrey.ac.uk
Wu, Xiao-Jun
Kittler, JosefJ.Kittler@surrey.ac.uk
Date : 3 June 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/TIP.2019.2919201
Grant Title : FACER2VM
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Clive Harris
Date Deposited : 13 Jun 2019 15:04
Last Modified : 13 Jun 2019 15:04
URI: http://epubs.surrey.ac.uk/id/eprint/851992

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