欢迎访问中国科学院大学学报,今天是
电子科学

基于V-CSK视频遥感卫星运动目标检测跟踪方法

  • 黄萍萍 ,
  • 王峰 ,
  • 向俞明 ,
  • 尤红建
展开
  • 1. 中国科学院空天信息研究院 中国科学院空间信息处理与应用系统技术重点实验室, 北京 100190;
    2. 中国科学院大学, 北京 100049

收稿日期: 2019-11-07

  修回日期: 2020-07-13

  网络出版日期: 2021-05-17

基金资助

中国科学院前沿科学重点研究计划(ZDBS-LY-JSC036)和国家自然科学基金(61901439)资助

Moving target detection and tracking of satellite videos based on V-CSK algorithm

  • HUANG Pingping ,
  • WANG Feng ,
  • XIANG Yuming ,
  • YOU Hongjian
Expand
  • 1. Key Laboratory of Spatial Information Processing and Application System of CAS, AeroSpace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2019-11-07

  Revised date: 2020-07-13

  Online published: 2021-05-17

摘要

视频遥感卫星在道路交通导航、军事动态监视、目标跟踪等方面具有众多应用,因此利用卫星视频实现运动目标实时检测跟踪备受关注。针对卫星视频中的运动目标检测跟踪问题,提出基于V-CSK算法的检测跟踪方法,该算法采用改进的ViBe检测算法,引入多重自定义阈值滤波算法获得运动目标中心区域,再根据中心位置均值原理与噪声距离判断原则提取目标中心坐标,并实现轨迹曲线估计和修正,最后利用CSK算法实现目标跟踪。基于3组卫星视频数据进行实验,同时引入目标检测和跟踪的对比实验作为参照。实验结果表明,V-CSK算法在卫星视频运动目标检测跟踪中具有良好的性能。

本文引用格式

黄萍萍 , 王峰 , 向俞明 , 尤红建 . 基于V-CSK视频遥感卫星运动目标检测跟踪方法[J]. 中国科学院大学学报, 2021 , 38(3) : 392 -401 . DOI: 10.7523/j.issn.2095-6134.2021.03.013

Abstract

Video remote sensing satellite has many applications in road traffic navigation, military dynamic monitoring, target tracking and so on, so the use of satellite video to achieve real-time detection and tracking of moving targets is of great concern. Aiming at the problem of moving target detection and tracking in satellite video, this paper proposes a detection and tracking method based on V-CSK algorithm, which adopts improved ViBe detection algorithm, introduces multiple custom threshold filtering algorithm to obtain the center area of moving target, and then extracts the center coordinates of target according to the principle of center position mean and noise distance judgment, and realizes the estimation and repair of track curve. Finally, the target tracking is realized by CSK algorithm. The experiment is based on three groups of satellite video data, and the contrast experiment of target detection and tracking is introduced as a reference. The experimental results show that V-CSK algorithm has good performance in satellite video moving target detection and tracking.

参考文献

[1] 刘韬. 国外视频卫星发展研究[J]. 国际太空, 2014, 36(9):50-56.
[2] 张过. 卫星视频处理与应用进展[J]. 应用科学学报, 2016, 34(4):361-370.
[3] 朱厉洪, 回征, 任德锋, 等. 视频成像卫星发展现状与启示[J]. 卫星应用, 2015, 6(10):23-28.
[4] 何胜皎. 视频序列中运动目标检测算法的研究[D].兰州:兰州理工大学, 2018.
[5] 韩露. 基于航拍图像的目标检测系统设计与实现[D]. 北京:北京理工大学, 2015.
[6] 孟琭, 杨旭. 目标跟踪算法综述[J]. 自动化学报, 2019, 45(7):1244-1260.
[7] 葛宝义, 左宪章, 胡永江. 视觉目标跟踪方法研究综述[J]. 中国图象图形学报, 2018, 23(8):1091-1107.
[8] 袁益琴. 遥感卫星视频图像车辆动态信息提取方法研究[D]. 北京:中国科学院大学(中国科学院遥感与数字地球研究所), 2017.
[9] Meng L F, Kerekes J P. Object tracking using high resolution satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1):146-152.
[10] 吴佳奇,张过,汪韬阳,等. 结合运动平滑约束与灰度特征的卫星视频点目标跟踪[J]. 测绘学报, 2017, 46(9):1135-1146.
[11] Du B, Sun Y J, Cai S H, et al. Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2):168-172.
[12] Du B, Cai S H, Wu C, et al. Object tracking in satellite videos based on a multiframe optical flow tracker[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8):3043-3055.
[13] Shao J, Du B, Wu C, et al. VCF:velocity correlation filter, towards space-borne satellite video tracking[C]//2018 IEEE International Conference on Multimedia and Expo(ICME), San Diego CA,USA:IEEE Press, 2018:1-6.
[14] Barnich O, Van Droogenbroeck M. ViBE:a powerful random technique to estimate the background in video sequences[C]//2019 IEEE Internationale Conference on Acoustics, Speech and Signal Processing, Taiwan, China:IEEE Press, 2009:945-948.
[15] Henriques J F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//12th European Conference on Computer Vision, Florence, Italy:Springer, 2012, 7575(4):702-715.
[16] 张文雅, 徐华中, 罗杰. 基于ViBe的复杂背景下的运动目标检测[J]. 计算机科学, 2017, 44(9):304-307.
[17] 蒋晶晶, 安博文. 低空航拍视频中基于Vibe算法的船舶检测方法[J]. 微型机与应用, 2017, 36(10):44-47.
[18] Henriques J F, Caseivo R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//European Conference on Computer Vision. Berlin, Germany:Springer, 2012:702-715.
[19] Zhang K H, Zhang L, Yang M H. Real-time compressive tracking[C]//12th European Conference on Computer Vision, Florence, Italy:Springer, 2012, 7574(3):864-877.
[20] Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77:125-141.
[21] Sevilla-Lara L, Learned-Miller E. Distribution fields for tracking[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:1910-1917.
[22] Wu Y, Shen B, Ling H B. Online robust image alignment via iterative convex optimization[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:1808-1814.
[23] Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:1822-1829.
[24] Bao C L, Wu Y, Ling H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:1830-1837.
[25] Zhang T Z, Ghanem B, Liu S, et al. Robust visual tracking via multi-task sparse learning[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:2042-2049.
[26] Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA:IEEE, 2012:1838-1845.
[27] Oron S, Bar-Hillel A, Levi D, et al. Locally orderless tracking[J]. International Journal of Computer Vision, 2015, 111(2):213-228.
文章导航

/