RGB-T image analysis technology and application: A survey

被引:24
|
作者
Song, Kechen [1 ,2 ,3 ]
Zhao, Ying [1 ,2 ,3 ]
Huang, Liming [1 ,2 ,3 ]
Yan, Yunhui [1 ,2 ,3 ]
Meng, Qinggang [4 ]
机构
[1] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Liaoning, Peoples R China
[2] Northeastern Univ, Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt, Shenyang 110819, Peoples R China
[3] Minist Educ, Key Lab Data Analyt & Optimizat Smart Ind, Shenyang 110819, Liaoning, Peoples R China
[4] Loughborough Univ, Dept Comp Sci, Loughborough LE11 3TU, England
基金
中国国家自然科学基金;
关键词
RGB-T images; Visible-thermal; Image fusion; Salient object detection; Pedestrian detection; Object tracking; Person re-identification; MODALITY PERSON REIDENTIFICATION; GENERATIVE ADVERSARIAL NETWORK; FUSION NETWORK; SEMANTIC SEGMENTATION; PEDESTRIAN DETECTION; SALIENCY DETECTION; ATTENTION NETWORK; SENSOR FUSION; FRAMEWORK; CONSISTENT;
D O I
10.1016/j.engappai.2023.105919
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent years. In the past decade, it has received wide attention and made a lot of important research progress in many applications. This paper provides a comprehensive review of RGB-T image analysis technology and application, including several hot fields: image fusion, salient object detection, semantic segmentation, pedestrian detection, object tracking, and person re-identification. The first two belong to the preprocessing technology for many computer vision tasks, and the rest belong to the application direction. This paper extensively reviews 400+ papers spanning more than 10 different application tasks. Furthermore, for each specific task, this paper comprehensively analyzes the various methods and presents the performance of the state-of-the-art methods. This paper also makes an in-deep analysis of challenges for RGB-T image analysis as well as some potential technical improvements in the future.
引用
收藏
页数:36
相关论文
共 50 条
  • [31] Bridging Search Region Interaction with Template for RGB-T Tracking
    Hui, Tianrui
    Xun, Zizheng
    Peng, Fengguang
    Huang, Junshi
    Wei, Xiaoming
    Wei, Xiaolin
    Dai, Jiao
    Han, Jizhong
    Liu, Si
    2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 13630 - 13639
  • [32] Learning cross-modal interaction for RGB-T tracking
    Chunyan XU
    Zhen CUI
    Chaoqun WANG
    Chuanwei ZHOU
    Jian YANG
    Science China(Information Sciences), 2023, 66 (01) : 320 - 321
  • [33] Enabling modality interactions for RGB-T salient object detection
    Zhang, Qiang
    Xi, Ruida
    Xiao, Tonglin
    Huang, Nianchang
    Luo, Yongjiang
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2022, 222
  • [34] Learning cross-modal interaction for RGB-T tracking
    Chunyan Xu
    Zhen Cui
    Chaoqun Wang
    Chuanwei Zhou
    Jian Yang
    Science China Information Sciences, 2023, 66
  • [35] Spatial exchanging fusion network for RGB-T crowd counting
    Rao, Chaoqun
    Wan, Lin
    NEUROCOMPUTING, 2024, 609
  • [36] A Lightweight RGB-T Fusion Network for Practical Semantic Segmentation
    Zhang, Haoyuan
    Li, Zifeng
    Wu, Zhenyu
    Wang, Danwei
    2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC, 2023, : 4233 - 4238
  • [37] Multimodal Feature-Guided Pretraining for RGB-T Perception
    Ouyang, Junlin
    Jin, Pengcheng
    Wang, Qingwang
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 16041 - 16050
  • [38] RGB-T显著性目标检测综述
    吴锦涛
    王安志
    任春洪
    红外技术, 2025, 47 (01) : 1 - 9
  • [39] Cross-modal collaborative propagation for RGB-T saliency detection
    Yu, Xiaosheng
    Pang, Yu
    Chi, Jianning
    Qi, Qi
    VISUAL COMPUTER, 2024, 40 (06): : 4337 - 4354
  • [40] AGFNet: Adaptive Gated Fusion Network for RGB-T Semantic Segmentation
    Zhou, Xiaofei
    Wu, Xiaoling
    Bao, Liuxin
    Yin, Haibing
    Jiang, Qiuping
    Zhang, Jiyong
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2025,