Advanced Search
中文
Volume 44 Issue 10
Oct.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
HE Min, HUI Bingwei, YI Mengni, HU Weidong. Infrared Moving-point Target Semi-Automatic Labeling Algorithm Based on Target Enhancement and Visual Tracking[J]. Infrared Technology , 2022, 44(10): 1073-1081.
Citation: HE Min, HUI Bingwei, YI Mengni, HU Weidong. Infrared Moving-point Target Semi-Automatic Labeling Algorithm Based on Target Enhancement and Visual Tracking[J]. Infrared Technology , 2022, 44(10): 1073-1081.
shu

Infrared Moving-point Target Semi-Automatic Labeling Algorithm Based on Target Enhancement and Visual Tracking

  • ATR Key Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

More Information
  • Received Date: October 10, 2021
  • Revised Date: December 07, 2021
    Graphical Abstract
    • Abstract
  • Infrared video data annotation has the problems of low efficiency and poor quality. In this paper, a semi-automatic labeling method for moving point targets in infrared sequence images is proposed based on target enhancement and visual tracking to solve it. First, infrared sequence images in a continuous period of time were registered and fused to enhance the target features. Second, a visual tracking algorithm was utilized to locate the fused features efficiently and automatically. Lastly, a saliency map was obtained through phase spectrum reconstruction, and the exact coordinates of a target were obtained. During automatic annotation, the difference between the annotation results of adjacent frames was used to select key frames, which enabled the annotators to locate the image frames that had errors and manually annotated them quickly. The results of the experiments showed that the algorithm significantly reduced the participation of annotators and effectively solved the problems of long period and poor quality assurance in data annotation.
    • FullText(HTML)
    • References (14)
  • [1]
    Yuen J, Russell B, Liu C, et al. Labelme video: building a video database with human annotations[C]// 12th International Conference on Computer Vision(ICCV), IEEE, 2009: 1451-1458.
    [2]
    Lee J H, Lee K S, Jo G S. Representation method of the moving object trajectories by interpolation with dynamic sampling[C]//2013 International Conference on Information Science and Applications (ICISA), IEEE, 2013: 1-4.
    [3]
    Gil-Jiménez P, Gómez-Moreno H, López-Sastre R, et al. Geometric bounding box interpolation: an alternative for efficient video annotation[J]. EURASIP Journal on Image and Video Processing, 2016, 2016(1): 1-13. DOI: 10.1186/s13640-015-0097-y
    [4]
    Vondrick C, Patterson D, Ramanan D. Efficiently scaling up crowdsourced video annotation[J]. International Journal of Computer Vision, 2013, 101(1): 184-204. DOI: 10.1007/s11263-012-0564-1
    [5]
    Vondrick C, Ramanan D. Video annotation and tracking with active learning[J]. Advances in Neural Information Processing Systems, 2011, 24: 28-36.
    [6]
    Buchanan A, Fitzgibbon A. Interactive feature tracking using K-D trees and dynamic programming[C]// IEEE Conference on Computer Vision & Pattern Recognition (CVPR), IEEE, 2006: 626-633.
    [7]
    Agarwala A, Hertzmann A, Salesin D H, et al. Key frame-based tracking for rotoscoping and animation[J]. ACM Transactions on Graphics, 2004, 23(3): 584-591. DOI: 10.1145/1015706.1015764
    [8]
    Biresaw T A, Nawaz T, Ferryman J, et al. Vitbat: video tracking and behavior annotation tool[C]//13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, 2016: 295-301.
    [9]
    Bakliwal P, Hegde G M, Jawahar C V. Collaborative Contributions for Better Annotations[C]//The International Conference on Computer Vision Theory and Applications(VISAPP), Scite Press, 2017: 353-360.
    [10]
    CHEN B, LING H, ZENG X, et al. Scribblebox: interactive annotation framework for video object segmentation[C]//European Conference on Computer Vision (ECCV), Berlin Springer, 2020: 293-310.
    [11]
    Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. DOI: 10.1023/B:VISI.0000029664.99615.94
    [12]
    Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(3): 583-596.
    [13]
    WANG M, LIU Y, HUANG Z. Large margin object tracking with circulant feature maps[C]//The IEEE Conference on Computer Vision and Pattern Recognition(CVPR), New York: IEEE, 2017: 4021-4029.
    [14]
    回丙伟, 宋志勇, 范红旗, 等. 地/空背景下红外图像弱小飞机目标检测跟踪数据集[J]. 中国科学数据, 2020, 5(3): 286-297. https://www.cnki.com.cn/Article/CJFDTOTAL-KXGZ202003030.htm

    HUI Bingwei, SONG Zhiyong, FAN Hongqi, et al. A dataset for infrared detection and tracking of dim-small aircraft targets under ground/air background[J]. China Sci. Data, 2020, 5(3): 286-297. https://www.cnki.com.cn/Article/CJFDTOTAL-KXGZ202003030.htm
    • Related Articles

  • Related Articles

    [1]CHEN Jia, YU Chengbo, WANG Shibing, JIANG Qichao, HE Xin, ZHANG Wei. IR Image Classification and Detection of Power Equipment Based on CBAM Improvement[J]. Infrared Technology , 2025, 47(1): 72-80.
    [2]WANG Xiaodong, LYU Tongfa, BAO Mingzheng, HE Yongchun, XIN Peng, WU Tao. Infrared Image Recognition Method for Power Equipment Based on Improved YOLO v5[J]. Infrared Technology , 2024, 46(6): 722-727.
    [3]HUANG Zhihong, XIAO Jian, XU Xianyong, ZHANG Hui. Spectral Residual Transformation for Thermal Defect Detection of Power Equipment[J]. Infrared Technology , 2023, 45(8): 884-889.
    [4]LIU Yunfeng, ZHAO Hongshan, YANG Jinbiao, HAN Jinfeng, LIU Bingcong. Super Resolution Method for Power Equipment Infrared Imaging Based on Gradient Norm-ratio Prior[J]. Infrared Technology , 2023, 45(1): 40-48.
    [5]LIANG Jian, HUANG Zhihong, ZHANG Keren. Multi-scale Guided Filter and Decision Fusion for Thermal Fault Diagnosis of Power Equipment[J]. Infrared Technology , 2022, 44(12): 1344-1350.
    [6]HUANG Zhihong, HONG Feng, HUANG Wei. Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments[J]. Infrared Technology , 2022, 44(8): 870-874.
    [7]SU Haifeng, ZHAO Yan, WU Zejun, CHENG Bo, LYU Linfei. Refined Infrared Object Detection Model for Power Equipment Based on Improved RetinaNet[J]. Infrared Technology , 2021, 43(11): 1104-1111.
    [8]HUANG Zhihong, WU Sheng, XIAO Jian, ZHANG Keren, HUANG Wei. Thermal Fault Diagnosis of Power Equipments Based on Guided Filter[J]. Infrared Technology , 2021, 43(9): 910-915.
    [9]WANG Xiaofang, MAO Huamin. Infrared Image Segmentation Method for Power Equipment in Complex Background[J]. Infrared Technology , 2019, 41(12): 1111-1116.
    [10]CHEN Jishun, XIAO Lijun, WAN Xinyu, MAI Ruijie, QIN Huiping, LI Lei. Research on Enhancement and Segmentation of Power Equipment Infrared Heat Map in Complex Environment[J]. Infrared Technology , 2018, 40(11): 1112-1118.

  • Cited by

    Periodical cited type(6)

    1. 王龙飞，胡雨旺，张泽光，刘悦，薛常喜. 非球面光学元件的快速制造技术. 光电工程. 2024(01): 3-34 .
    2. 刘斌，王孝坤，程强，刘忠凯，李凌众，蔡梦雪，张洛嘉，李文涵，王金成，张学军. 复杂曲面光学元件高精度面形检测技术概述. 南通大学学报(自然科学版). 2024(01): 1-27 .
    3. 席建普，周卓林，罗来华，张通通，聂子凯. 自由曲面轮廓仪机架多目标优化设计. 制造技术与机床. 2024(05): 122-127 .
    4. 简恩霖，肖红军，张芷芊，杨皓桦，吴南寿，曾亚光. 谱域OCT技术在非球面镜面型测量中的应用. 现代信息科技. 2024(09): 58-61 .
    5. 叶卉，王泽华，许世炜，姜晨. 基于便携式粗糙度仪的表面轮廓测量实验平台设计. 实验技术与管理. 2024(07): 140-147 .
    6. 王兆明，栗孟娟，于秋跃，李春林，赵子跃，王经华，吕天斌，张兆健，于长锁. 两面共体非球面反射镜光轴一致性高精度测量方法研究(特邀). 红外与激光工程. 2023(09): 39-46 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML
    Article views (129) PDF downloads (35) Cited by(8)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return