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
  • [21] MMNet: Multi-modal multi-stage network for RGB-T image semantic segmentation
    Lan, Xin
    Gu, Xiaojing
    Gu, Xingsheng
    APPLIED INTELLIGENCE, 2022, 52 (05) : 5817 - 5829
  • [22] Feature aggregation with transformer for RGB-T salient object detection
    Zhang, Ping
    Xu, Mengnan
    Zhang, Ziyan
    Gao, Pan
    Zhang, Jing
    NEUROCOMPUTING, 2023, 546
  • [23] FEATURE ENHANCEMENT AND FUSION FOR RGB-T SALIENT OBJECT DETECTION
    Sun, Fengming
    Zhang, Kang
    Yuan, Xia
    Zhao, Chunxia
    2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2023, : 1300 - 1304
  • [24] Region Selective Fusion Network for Robust RGB-T Tracking
    Yu, Zhencheng
    Fan, Huijie
    Wang, Qiang
    Li, Ziwan
    Tang, Yandong
    IEEE SIGNAL PROCESSING LETTERS, 2023, 30 : 1357 - 1361
  • [25] MMNet: Multi-modal multi-stage network for RGB-T image semantic segmentation
    Xin Lan
    Xiaojing Gu
    Xingsheng Gu
    Applied Intelligence, 2022, 52 : 5817 - 5829
  • [26] Detecting Eating and Social Presence with All DayWearable RGB-T
    Shahi, Soroush
    Sen, Sougata
    Pedram, Mahdi
    Alharbi, Rawan
    Gao, Yang
    Katsaggelos, Aggelos K.
    Hester, Josiah
    Alshurafa, Nabil
    2023 IEEE/ACM CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE, 2023, : 68 - 79
  • [27] Revisiting Feature Fusion for RGB-T Salient Object Detection
    Zhang, Qiang
    Xiao, Tonglin
    Huang, Nianchang
    Zhang, Dingwen
    Han, Jungong
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 31 (05) : 1804 - 1818
  • [28] Learning a Twofold Siamese Network for RGB-T Object Tracking
    Kuai, Yangliu
    Li, Dongdong
    Qian, Que
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2021, 30 (05)
  • [29] Learning cross-modal interaction for RGB-T tracking
    Xu, Chunyan
    Cui, Zhen
    Wang, Chaoqun
    Zhou, Chuanwei
    Yang, Jian
    SCIENCE CHINA-INFORMATION SCIENCES, 2023, 66 (01)
  • [30] Scribble-Supervised RGB-T Salient Object Detection
    Liu, Zhengyi
    Huang, Xiaoshen
    Zhang, Guanghui
    Fang, Xianyong
    Wang, Linbo
    Tang, Bin
    2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME, 2023, : 2369 - 2374