基于改进Camshift与Kalman滤波融合的领航车辆跟踪算法  

Pilot vehicle tracking algorithm based on improved Camshift and Kalman filter fusion

作　　者：李建良[1] 张婷婷 陶知非[2] 李淑清[1] 郭秋蕊 Li Jianliang;Zhang Tingting;Tao Zhifei;Li Shuqing;Guo Qiurui(College of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300222,China;Bureau of Geophysical Prospecting INC,China National Petroleum Corporation,Zhuozhou 072750,China)

机构地区：[1]天津科技大学电子信息与自动化学院,天津300222 [2]中国石油集团东方地球物理勘探有限责任公司,涿州072750

出　　处：《电子测量与仪器学报》2021年第6期131-139,共9页Journal of Electronic Measurement and Instrumentation

摘　　要：针对智能车辆在对前方领航车辆进行视觉跟踪时,传统的Camshift算法容易受目标突然变速、相似颜色背景或目标干扰的问题,提出一种基于改进Camshift与Kalman滤波融合的领航车辆跟踪算法。该算法通过提取目标模板的色度、饱和度和Canny边缘梯度幅值3个特征分量,建立三维直方图并对其反向投影进行跟踪,同时采用Bhattachayya系数作为目标跟踪准确性的判别依据。若系数大于设定阈值则判定目标跟踪不准确,此时用局部二值模式(LBP)级联分类器对领航车辆进行检测识别,最后引入Kalman滤波器来预测下一帧领航车辆的位置。实验结果表明,该算法能够在复杂背景下对领航车辆进行实时并有效的跟踪。Aiming at the problem that the traditional Camshift algorithm is susceptible to the sudden acceleration and deceleration of the target and the background or the target interference of the similar colors when the intelligent vehicle is visually tracking the pilot vehicle in front,a pilot vehicle tracking algorithm that combines the improved Camshift and Kalman filter was proposed.The algorithm tracked the back projection of the three-dimensional histogram established by the target template hue,saturation,and edge gradient amplitude feature components.The Bhattachayya coefficient was used as the basis for determining the accuracy of target tracking.If the coefficient was greater than the set threshold,the target tracking would be judged to be inaccurate.At this time,the LBP cascade classifier was used to detect and recognize the pilot vehicle,and finally the Kalman filter was introduced to predict the position of the pilot vehicle in the next frame.The experimental results demonstrate that the proposed algorithm can accurately track the pilot vehicle in real time in a complex background.

关 键 词：领航车辆跟踪 CAMSHIFT算法 Bhattachayya系数 Kalman滤波 边缘梯度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TN911.7[自动化与计算机技术—计算机科学与技术]