液晶与显示2018，Vol.33Issue(12)：1040-1046,7.DOI:10.3788/YJYXS20183312.1040
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
摘要
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
机器视觉/目标跟踪/红外图像/OCS-LBP特征/核相关滤波Key words
machine vision/target tracking/infrared image/OCS-LBP feature/kernel correlation filter分类
信息技术与安全科学引用本文复制引用
卢杨,张磊,郭立媛,杜若鹏..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):1040-1046,7.基金项目
河北省科技计划项目(No.16210315D) （No.16210315D）
