| 引用本文: | 钟立军,于起峰,周颉鑫,等.基于位置预测的靶场图像实时判读方法.[J].国防科技大学学报,2020,42(2):85-91.[点击复制] |
| ZHONG Lijun,YU Qifeng,ZHOU Jiexin,et al.Real-time interpretation method for range image based on position prediction[J].Journal of National University of Defense Technology,2020,42(2):85-91[点击复制] |
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| 基于位置预测的靶场图像实时判读方法 |
| 钟立军1,于起峰1,周颉鑫1,郭鹏宇2,黄维3 |
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(1. 国防科技大学 空天科学学院, 湖南 长沙 410073;2. 军事科学院 国防科技创新研究院, 北京 100071;3.中国人民解放军95975部队, 甘肃 酒泉 732750)
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| 摘要: |
| 在靶场经纬仪对目标实时跟踪测量时,会发生相机随机抖动的情况,引起目标在图像中大幅度运动。应对大幅度运动时,基于搜索窗口的跟踪方法容易丢失目标,而基于全图搜索的跟踪方法时效性差。针对以上问题,提出一种结合核相关滤波算法(Kernelized Correlation Filter, KCF)和目标位置预测的改进的跟踪学习检测算法(Tracking-Learning-Detection, TLD)跟踪框架。利用正交多项式最优线性滤波器及相机角度信息预测目标下一帧位置,在此区域利用KCF进行快速跟踪,可以提高跟踪的成功率和时效性,跟踪失败时再进行检测。仿真实验表明,最优线性滤波器能较准确预测目标位置,给KCF提供较准确的搜索位置,算法每帧耗时仅为1.1 ms,且定位精度优于TLD和KCF,能有效应对相机抖动的问题。靶场实际试验证明该方法可提高靶场自动判读水平,减少人工干预。 |
| 关键词: 实时判读 改进TLD 位置预测 最优线性滤波器 |
| DOI：10.11887/j.cn.202002011 |
| 投稿日期：2019-04-22 |
| 基金项目:国家自然科学基金资助项目(11472302) |
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| Real-time interpretation method for range image based on position prediction |
| ZHONG Lijun1, YU Qifeng1, ZHOU Jiexin1, GUO Pengyu2, HUANG Wei3 |
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(1.College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China;2.National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China;3.The PLA Unit 95975, Jiuquan 732750, China)
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| Abstract: |
| When the shooting range′s optical theodolite tracks the target in real time, the camera will randomly jitter, causing the target large-scale motion in the image. In dealing with large-scale motion, the tracking method based on the search window is easy to lose the target, and the tracking method based on the full-image search is time-consuming. Considering these problems, an improved TLD(tracking-learning-detection) framework combining KCF(kernelized correlation filter) and target position prediction was proposed. An orthogonal polynomial optimal linear filter and camera angle information were utilized to predict the position of the next frame of the target, and KCF was used for fast-tracking in this area, which can improve the success rate and save the tracking time, and can detect when the tracking fails. Simulation experiments demonstrate that the optimal linear filter can accurately predict the target position and provide KCF with a more accurate search position. Besides, the algorithm consumes only 1.1ms per frame, and the positioning accuracy is better than that of TLD and KCF, which can effectively copes with camera′s jitter. The actual task verification proves that this method can improve the automatic interpretation level of the shooting range and reduce manual intervention. |
| Keywords: real-time interpretation improved tracking-learning-detection position prediction optimal linear filter |
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