Attentional correlation filter network for adaptive visual tracking

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Title: Attentional correlation filter network for adaptive visual tracking
Authors: Choi, J
Chang, HJ
Yun, S
Fischer, T
Demiris, Y
Choi, JY
Item Type: Conference Paper
Abstract: We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.
Issue Date: 9-Nov-2017
Date of Acceptance: 27-Feb-2017
ISSN: 1063-6919
Publisher: IEEE
Journal / Book Title: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on
Copyright Statement: © 2017 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.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 612139
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition
Publication Status: Published
Start Date: 2017-07-22
Finish Date: 2017-07-26
Conference Place: Honolulu, Hawaii, US
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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