基于尺度搜索的车辆跟踪算法  

Vehicle Tracking Algorithm Based on Scale Search

作　　者：范永昆 张正道[1] 彭力[1] Fan Yongkun;Zhang Zhengdao;Peng Li(School of Internet of Things,Jiangnan University,Wuxi,Jiangsu 214122,China)

机构地区：[1]江南大学物联网工程学院

出　　处：《激光与光电子学进展》2019年第22期119-127,共9页Laser & Optoelectronics Progress

基　　金：教育部-中国移动科研基金(MCM20170204)

摘　　要：为解决视觉车辆跟踪过程中尺度变化导致的模型漂移问题,在核相关滤波算法的基础上,提出一种车辆目标的尺度搜索算法。通过比较目标区域上三个特定尺度相关滤波响应的平均峰值相关能量,推断出目标尺度的变化方向,进而在变化方向上迭代搜索当前目标的最佳尺度。此外,为确保相关滤波模板能够适应运动过程中的车辆外观变化,在最佳尺度估计的情形下,对模板进行自适应加权更新,进一步提高模板准确性。大量的实验表明,本文算法可以有效解决车辆跟踪过程中尺度变化导致的模型漂移问题,且相比于其他相关滤波类算法具有更加优秀的跟踪性能。To solve the problem of model drift caused by scale variation in visual vehicle tracking,this study proposes a scale search method for the vehicle target based on a kernelized correlation filtering algorithm.The change direction of the target scale is deduced by comparing the average peak related energy of correlation filtering responses obtained from object regions with three given scales,followed by an iterative search for the best scale of the current target in the change direction.To ensure that the correlation filtering template can adapt to the change of vehicle appearance in the process of motion,the template is upgraded with adaptive weight under the condition of the best scale estimation.The manner of adaptive weighting further improves the accuracy of the template.Numerous experiments show that the proposed method effectively solves the problem of model drift caused by scale change in vehicle tracking and provides better tracking performance than other correlation filtering algorithms.

关 键 词：机器视觉 车辆跟踪 尺度搜索 平均峰值相关能量 自适应模板更新 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]