Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44100
Title: Modality-Correlation-Aware Sparse Representation for RGB-Infrared Object Tracking
Authors: Lan, X
Ye, M
Zhang, S
Zhou, H
Yuen, PC
First Published: 9-Oct-2018
Publisher: Elsevier for International Association for Pattern Recognition, North-Holland
Citation: Pattern Recognition Letters, 2018
Abstract: To intelligently analyze and understand video content, a key step is to accurately perceive the motion of the interested objects in videos. To this end, the task of object tracking, which aims to determine the position and status of the interested object in consecutive video frames, is very important, and has received great research interest in the last decade. Although numerous algorithms have been proposed for object tracking in RGB videos, most of them may fail to track the object when the information from the RGB video is not reliable (e.g. in dim environment or large illumination change). To address this issue, with the popularity of dual-camera systems for capturing RGB and infrared videos, this paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking. Specifically, this proposed model is able to (1) perform feature representation of objects in different modalities by employing the robustness of sparse representation, and (2) combine the representation by exploiting the modality correlation. Extensive experiments demonstrate the effectiveness of the proposed method.
DOI Link: 10.1016/j.patrec.2018.10.002
ISSN: 0167-8655
Links: https://www.sciencedirect.com/science/article/pii/S0167865518307633
http://hdl.handle.net/2381/44100
Embargo on file until: 9-Oct-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier for International Association for Pattern Recognition, North-Holland 2018. After an embargo period this version of the paper will be an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Computer Science

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