基于改进纹理特征的红外目标跟踪算法

卢杨; 张磊; 郭立媛; 杜若鹏

doi:10.3788/YJYXS20183312.1040

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基于改进纹理特征的红外目标跟踪算法

图像处理 | 更新时间：2020-08-12

- 基于改进纹理特征的红外目标跟踪算法
- Infrared target tracking based on improved LBP feature
- 液晶与显示 2018年33卷第12期页码：1040-1046
- 作者机构：
  
  河北工业大学人工智能与数据科学学院, 天津 300130
- 作者简介：
  
  [ "卢杨(1993-), 男, 天津市人, 硕士研究生, 2016年于河北工业大学获得学士学位, 主要从事机器视觉、目标跟踪方面的研究。E-mail:460280163@qq.com" ]
  [ "张磊(1977-), 男, 天津市人, 教授, 硕士生导师, 1999年于南开大学获得学士学位, 2005年于北京航空航天大学获得博士学位(硕博连读), 主要从事智能控制和图像处理的研究。E-mail:zhanglei@hebut.edu.cn" ]
- 基金信息：
  
  河北省科技计划项目(16210315D);Supported by Hebei Science and Technology Planning Project(16210315D)
- DOI：10.3788/YJYXS20183312.1040
  中图分类号： TP391
- 收稿日期：2018-06-20，
  
  录用日期：2018-8-3，
  
  纸质出版日期：2018-12
- 稿件说明：
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卢杨, 张磊, 郭立媛, 等. 基于改进纹理特征的红外目标跟踪算法[J]. 液晶与显示, 2018,33(12):1040-1046.

Yang LU, Lei ZHANG, Li-yuan GUO, et al. Infrared target tracking based on improved LBP feature[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1040-1046.
卢杨, 张磊, 郭立媛, 等. 基于改进纹理特征的红外目标跟踪算法[J]. 液晶与显示, 2018,33(12):1040-1046. DOI： 10.3788/YJYXS20183312.1040.

Yang LU, Lei ZHANG, Li-yuan GUO, et al. Infrared target tracking based on improved LBP feature[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1040-1046. DOI： 10.3788/YJYXS20183312.1040.

摘要

针对红外图像背景复杂、杂波干扰严重、相似目标混淆导致的目标跟踪丢失问题，本文提出了一种改进的低维度纹理特征OCS-LBP（Oriented Center Symmetric Local Binary Patterns，即方向中心对称的局部二值模式）。首先，利用此特征可以高效地获取目标图像中每个像素块的梯度方向和幅值信息，提高了跟踪过程的鲁棒性；其次，利用核相关滤波算法结合提取的OCS-LBP特征对目标图像区域进行模型训练；最后，根据训练好的模型检测下一帧图像中目标的具体位置。本文在10组红外视频序列上进行了测试，实验结果表明，本文算法的精确度和成功率相比于第二名算法分别获得了2.9%和9.9%的提升，同时在实验设备上算法的平均跟踪速度相比于第二名算法提升了14.15 frame/s。从实验结果可以看出本文提出的算法在红外目标跟踪上表现出较好的鲁棒性、准确性和实时性，具有一定的研究和实用价值。

Abstract

In order to solve the problem that loss of target caused by complex background

serious clutter interference and similar target confusion in infrared image tracking

this paper proposes an improved low-dimensional texture feature OCS-LBP (Oriented Center Symmetric Local Binary Patterns). First

according to this feature

the gradient direction and amplitude information of each pixel block in the target image can be efficiently acquired

which improves the robustness of the infrared target tracking. Secondly

the kernel correlation filter algorithm combined with the OCS-LBP features is used from infrared target image to training model. Finally

based on the trained model

the target can be detected in the next frame of infrared image. In this paper

the proposed algorithm is tested for 10 video sequences. The result shows that the accuracy and the success rate of the proposed algorithm are improved by 2.9% and 9.9% higher than the second algorithm respectively. At the same time

the average tracking speed of the proposed algorithm on the computer improves 14.15 frame/s. From the experimental results

it can be seen that OCS-LBP feature has better robustness

accuracy and real-time performance in infrared target tracking than traditional features

which has certain research and practical value.

关键词

Keywords

references

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