吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (3): 680-687.doi: 10.13229/j.cnki.jdxbgxb20180201

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面向车辆自主驾驶的行人跟踪算法

李志慧1(),钟涛1,赵永华2(),胡永利1,李海涛1,赵景伟3   

  1. 1. 吉林大学 交通学院, 长春 130022
    2. 吉林大学 公共计算机公共教学与研究中心, 长春 130022
    3. 长春市公安局交警支队, 长春 130000
  • 收稿日期:2018-03-07 出版日期:2019-05-01 发布日期:2019-07-12
  • 通讯作者: 赵永华 E-mail:lizhih@jlu.edu.cn;zhaoyonghua@jlu.edu.cn
  • 作者简介:李志慧(1977?)，男，副教授，博士. 研究方向：交通视频检测与处理. E?mail:lizhih@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51278220)

Pedestrian tracking algorithm for autonomous driving

Zhi⁃hui LI1(),Tao ZHONG1,Yong⁃hua ZHAO2(),Yong⁃li HU1,Hai⁃tao LI1,Jing⁃wei ZHAO3   

  1. 1. College of Transportation, Jilin University, Changchun 130022，China
    2. Public Computer Education and Research Center, Jilin University, Changchun 130022，China
    3. Traffic Police Division of Public Security Bureau, Changchun 130000，China
  • Received:2018-03-07 Online:2019-05-01 Published:2019-07-12
  • Contact: Yong?hua ZHAO E-mail:lizhih@jlu.edu.cn;zhaoyonghua@jlu.edu.cn

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摘要：

基于背景感知相关滤波框架和车辆前方行人运动的特点，建立了运动行人尺度快速估计和选择性更新的行人跟踪算法。首先，在线训练学习待跟踪行人的背景感知相关滤波器。其次，针对行人的尺度变化训练一个一维的尺度相关滤波器对尺度进行精细搜索，使算法更适应车载的快速尺度变化。再次，利用峰值旁瓣比评价行人状态，建立两相关滤波器的选择性更新机制。最后，基于吉林大学车载试验数据库JLU?PDS、德国奔驰Daimler、美国OTB共享国际测试库，与卡尔曼车载行人跟踪算法进行对比测试，试验结果表明本文算法具有较好的尺度适应和抗遮挡性能，更能满足车辆自主驾驶的需求。

关键词: 交通运输系统工程, 车辆自主驾驶, 行人跟踪, 背景感知相关滤波, 尺度估计, 选择性更新

Abstract:

Pedestrian tracking is the base for pedestrian behavior analysis and driving decision?making. There exist several problems such as the fast scale change of pedestrian caused by rapid movement of vehicle, the pedestrian occlusion, etc, which make the traditional tracking algorithm difficult to track pedestrians accurately, and it is more difficult to analyze pedestrian movement. To overcome the problems, a new pedestrian tracking method is presented in the paper. In the method, scale estimation and selective updating strategy were used to deal with fast scale change of pedestrian and occluded in the framework of background?aware correlation filter. First, a background?aware correlation filter was trained online for the pedestrian to be tracked. Secondl an one?dimensional scale correlation filter was trained to search the scale of pedestrians carefully, so that the algorithm is more adaptable to fast scale change in the case of vehicle driving. Finally, a selective updating mechanism of the correlation filters was set up by using the peak sidelobe ratio to evaluate the pedestrian status. In experiments, the proposed method was compared with Kalman filter pedestrian tracking algorithm on JLU?PDS, Daimler Pedestrian Benchmark Data Set and some video sequences from Object Tracking Benchmark. The results show that the proposed method has better scale adaptation and anti occlusion performance, and it is more adaptable to the autonomous driving.

Key words: engineering of communication and transportation system, autonomous car, pedestrian tracking, background?aware correlation filter, scale estimation, selective model updating

中图分类号: 

  • U495

图1

尺度变化试验对比图"

图2

遮挡处理对比试验图"

图3

戴姆勒数据库部分试验结果图"

图4

吉林大学校园内拍摄视频部分结果图"

图5

两种算法的成功率"

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