Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tr...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-12, Vol.66 (12), p.9887-9897
Main Authors: Lan, Xiangyuan, Ye, Mang, Shao, Rui, Zhong, Bineng, Yuen, Pong C., Zhou, Huiyu
Format: Article
Language:English
Subjects:
Algorithms
Closed circuit television
Computational modeling
Consistency
Correlation
Feature extraction
Infrared tracking
Machine learning
Mathematical model
Multimodal sensor fusion
Object tracking
Representations
Surveillance systems
Target tracking
Task analysis
tracking system
Tracking systems
video surveillance system
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Summary:With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tracking performance and robustness of these systems may be degraded dramatically when the information from RGB video is unreliable (e.g., poor illumination conditions or very low resolution). To address this issue, this paper presents a new tracking system, which aims to combine the information from RGB and infrared modalities for object tracking. The proposed tracking systems is based on our proposed machine learning model. Particularly, the learning model can alleviate the modality discrepancy issue under the proposed modality consistency constraint from both representation patterns and discriminability, and generate discriminative feature templates for collaborative representations and discrimination in heterogeneous modalities. Experiments on a variety of challenging RGB-infrared videos demonstrate the effectiveness of the proposed algorithm.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2019.2898618