改进的CamShift无人机目标跟踪算法  

Improved CamShift UAV Target Tracking Algorithm

作　　者：李睿[1] 商家赫 LI Rui;SHANG Jia-he(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,甘肃兰州730050

出　　处：《计算机与现代化》2020年第11期83-88,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(61761028);甘肃省科技计划项目(18YF1GA060)。

摘　　要：目前,无人机视频目标跟踪算法在应用方面仍存在一些问题,比如在光照不均、目标发生旋转、目标被遮挡的情况下跟踪效果不佳。因此,本文提出一种结合HLBP特征匹配与Kalman滤波的CamShift跟踪算法。首先通过HLBP算法对目标特征进行提取,获得更准确的纹理特征,进而减小光照变化以及目标旋转对特征提取造成的干扰,其次通过巴氏距离对目标遮挡程度进行判断,最后结合Kalman滤波算法对目标位置进行预测,能够有效解决目标发生遮挡时跟踪效果不佳的问题。实验结果表明,在无人机目标跟踪的实际应用中,改进算法能够有效降低外在干扰对跟踪效果的影响,跟踪精度得到提升。At present,there are still some problems in the application of UAV video target tracking algorithms.For example,the tracking effect is not good when the illumination is uneven,the target rotates,and the target is blocked.Therefore,this paper proposes a CamShift tracking algorithm combining HLBP feature matching with Kalman filtering.First,the target features are extracted by the HLBP algorithm to obtain more accurate texture features,and then the interference caused by the change in illumination and the target rotation to the feature extraction is reduced.Second,the degree of occlusion of the target is judged by Bhattacharyya distance.Finally,the Kalman filter algorithm is used in the prediction of the target position,which can effectively solve the problem of poor tracking effect when the target is blocked.The experimental results show that in the practical application of UAV target tracking,the improved algorithm can effectively reduce the impact of external interference on the tracking effect,and the tracking accuracy is improved.

关 键 词：无人机 目标跟踪 CAMSHIFT跟踪算法 HLBP纹理处理 Kalman滤波算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]