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基于多特征融合与分层数据关联的空中红外多目标跟踪方法
引用本文:杨博,蔺素珍,禄晓飞,李大威,秦品乐,左健宏. 基于多特征融合与分层数据关联的空中红外多目标跟踪方法[J]. 计算机应用, 2020, 40(10): 3075-3080. DOI: 10.11772/j.issn.1001-9081.2020030320
作者姓名:杨博  蔺素珍  禄晓飞  李大威  秦品乐  左健宏
作者单位: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
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  
收稿时间:2020-03-20
修稿时间:2020-05-20

Multiple aerial infrared target tracking method based on multi-feature fusion and hierarchical data association
YANG Bo,LIN Suzhen,LU Xiaofei,LI Dawei,QIN Pinle,ZUO Jianhong. Multiple aerial infrared target tracking method based on multi-feature fusion and hierarchical data association[J]. Journal of Computer Applications, 2020, 40(10): 3075-3080. DOI: 10.11772/j.issn.1001-9081.2020030320
Authors:YANG Bo  LIN Suzhen  LU Xiaofei  LI Dawei  QIN Pinle  ZUO Jianhong
Affiliation:1. School of Big Data, North University of China, Taiyuan Shanxi 030051, China;2. Jiuquan Satellite Launch Center, Jiuquan Gansu 735000, China
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.
Keywords:target tracking  aerial target  infrared multiple target  data association  multi-feature fusion  
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