Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 3075-3080.DOI: 10.11772/j.issn.1001-9081.2020030320

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Multiple aerial infrared target tracking method based on multi-feature fusion and hierarchical data association

YANG Bo1, LIN Suzhen1, LU Xiaofei2, LI Dawei1, QIN Pinle1, ZUO Jianhong1   

  1. 1. School of Big Data, North University of China, Taiyuan Shanxi 030051, China;
    2. Jiuquan Satellite Launch Center, Jiuquan Gansu 735000, China
  • Received:2020-03-20 Revised:2020-05-20 Online:2020-10-10 Published:2020-06-08
  • Supported by:
    This work is partially supported by the Applied Basic Research Project of Shanxi Province (201901D111151).


杨博1, 蔺素珍1, 禄晓飞2, 李大威1, 秦品乐1, 左健宏1   

  1. 1. 中北大学 大数据学院, 太原 030051;
    2. 酒泉卫星发射中心, 甘肃 酒泉 735000
  • 通讯作者: 蔺素珍
  • 作者简介:杨博(1996-),男,山西临汾人,硕士研究生,CCF会员,主要研究方向:多目标跟踪、计算机视觉;蔺素珍(1966-),女,山西灵石人,教授,博士,CCF会员;主要研究方向:图像处理、机器视觉;禄晓飞(1981-),男,河南许昌人,博士,主要研究方向:测量数据处理;李大威(1980-),男,河北衡水人,讲师,博士,主要研究方向:模式识别、机器学习、图像处理;秦品乐(1978-),男,山西长治人,教授,博士,CCF会员,主要研究方向:医学影像大数据存储、分析及可视化;左建宏(1995-),男,山西临汾人,硕士研究生,主要研究方向:红外小目标检测、红外小目标跟踪。
  • 基金资助:

Abstract: An online multiple target tracking method for the aerial infrared targets was proposed based on the hierarchical data association to solve the tracking difficulty caused by the high similarity, large number and large false detections of the targets in star background. Firstly, according to the characteristics of the infrared scene, the location features, gray features and scale features of the targets were extracted. Secondly, the above three features were combined to calculate the preliminary relationship between the targets and the trajectories in order to obtain the real targets. Thirdly, the obtained real targets were classified according to their scales. The large-scale target data association was calculated by adding three features of appearance, motion and scale. The small-scale target data association was calculated by multiplying the two features of appearance and motion. Finally, the target assignment and trajectory updating were performed to the two types of targets respectively according to the Hungarian algorithm. Experimental results in a variety of complex conditions show that:compared with the online tracking method only using motion features, the proposed method has the tracking accuracy improved by 12.6%; compared with the method using multi-feature fusion, the hierarchical data correlation of the proposed method not only improves the tracking speed, but also increases the tracking accuracy by 19.6%. In summary, this method not only has high tracking accuracy, but also has good real-time performance and anti-interference ability.

Key words: target tracking, aerial target, infrared multiple target, data association, multi-feature fusion

摘要: 针对星空背景下目标相似度高、数量大和误检数目较多所导致的空中红外多目标跟踪困难问题,提出基于分层数据关联的空中红外多目标在线跟踪方法。首先,根据红外场景特性来提取目标的位置特征、灰度特征和尺度特征;其次,综合这三个特征来计算目标与轨迹之间的初步关联关系以获得真实目标;再次,将所获得的真实目标按照尺度大小分类,大尺度类目标数据关联采用表观特征、运动特征、尺度特征三种特征相加的方法来计算,小尺度类目标数据关联采用表观特征与运动特征两种特征相乘的方法来计算;最后,根据匈牙利算法对两类目标分别进行目标分配、完成轨迹更新。多种复杂情况下的实验结果表明:与仅采用运动特征的在线跟踪方法相比,所提方法的跟踪准确率提升了12.6%;与采用多特征融合的方法相比,所提方法的分层数据关联不仅提高了跟踪速度,也使跟踪准确率提升了19.6%。综上,该方法不仅跟踪精度高,而且具有较好的实时性和抗干扰能力。

关键词: 目标跟踪, 空中目标, 红外多目标, 数据关联, 多特征融合

CLC Number: