Comparison of Different Level Fusion Schemes for Infrared-Visible Object Tracking: An Experimental Survey

被引:0
|
作者
Luo, Chengwei [1 ]
Sun, Bin [1 ]
Deng, Qiao [1 ]
Wang, Zihao [1 ]
Wang, Dengwei [1 ]
机构
[1] UESTC, Sch Aeronaut & Astronaut, Chengdu, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
infrared-visible; pixel-level; feature-level; decision-level; correlation filter; fusion tracking; IMAGE FUSION; REGISTRATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Infrared and visible image fusion tracking is now revealing its great potential and breeding a qualitative leap in visual object tracking field. Valuable complementary information provided by infrared and visible images is the key of improving tracking performance and plays a critical role when facing multiple complex challenges. In this paper, a summary for datasets of infrared and visible image sequences is intentionally made to conclude our preparatory work for building a standard benchmark. Besides, a general framework of fusion tracking on three different levels is proposed to conclude the fusion tracking schemes for the first time, which can generally include all the fusion tracking algorithms. Extensive experiments under complex scenarios have been conducted to evaluate the performances of the three fusion tracking schemes. The results show the significance of the fusion tracking on infrared and visible image sequences. Finally, this survey proactively concludes future research directions and potential improvements in this field.
引用
收藏
页码:23 / 29
页数:7
相关论文
共 50 条
  • [31] A Novel Teacher-Student Framework With Degradation Model for Infrared-Visible Image Fusion
    Xue, Weimin
    Liu, Yisha
    Wang, Fei
    He, Guojian
    Zhuang, Yan
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 12
  • [32] A DT-CWT-based infrared-visible image fusion method for smart city
    Qi G.
    Zheng M.
    Zhu Z.
    Yuan R.
    International Journal of Simulation and Process Modelling, 2019, 14 (06) : 559 - 570
  • [33] Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution
    Xiao, Wanxin
    Zhang, Yafei
    Wang, Hongbin
    Li, Fan
    Jin, Hua
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [34] The optical design of rapid infrared-visible mufti-object spectrometer: a NGST demonstration instrument
    Ge, J
    IR SPACE TELESCOPES AND INSTRUMENTS, PTS 1 AND 2, 2003, 4850 : 535 - 543
  • [35] Feature-Level Fusion Algorithm of Infrared Image and Visible Image for Object Identification in the Forest
    Yu, Zheng
    Zhang, Yuanyuan
    Ding, Xiaokang
    Zhu, Yuting
    Yan, Lei
    INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL AND AUTOMATION (ICECA 2014), 2014, : 701 - 705
  • [36] Discriminative Fusion Correlation Learning for Visible and Infrared Tracking
    Yun, Xiao
    Sun, Yanjing
    Yang, Xuanxuan
    Lu, Nannan
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 2019
  • [37] Visible-infrared fusion schemes for road obstacle classification
    Apatean, Anca
    Rogozan, Alexandrina
    Bensrhair, Abdelaziz
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 35 : 180 - 192
  • [38] INFRARED-VISIBLE IMAGE FUSION USING THE UNDECIMATED WAVELET TRANSFORM WITH SPECTRAL FACTORIZATION AND TARGET EXTRACTION
    Ellmauthaler, Andreas
    da Silva, Eduardo A. B.
    Pagliari, Carla L.
    Neves, Sergio R.
    2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 2661 - 2664
  • [39] NSMT: A Novel Non-subsampled Morphological Transform Fusion Algorithm for Infrared-Visible Images
    Hu, Peng
    Wang, Chenjun
    Li, Dequan
    Zhao, Xin
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2024, 43 (02) : 1298 - 1318
  • [40] A perceptual framework for infrared-visible image fusion based on multiscale structure decomposition and biological vision
    Zhou, Zhiqiang
    Fei, Erfang
    Miao, Lingjuan
    Yang, Rao
    INFORMATION FUSION, 2023, 93 : 174 - 191