基于激光雷达和相机信息融合的目标检测及跟踪

常昕, 陈晓冬, 张佳琛, 等. 基于激光雷达和相机信息融合的目标检测及跟踪[J]. 光电工程, 2019, 46(7): 180420. doi: 10.12086/oee.2019.180420
引用本文: 常昕, 陈晓冬, 张佳琛, 等. 基于激光雷达和相机信息融合的目标检测及跟踪[J]. 光电工程, 2019, 46(7): 180420. doi: 10.12086/oee.2019.180420
Chang Xin, Chen Xiaodong, Zhang Jiachen, et al. An object detection and tracking algorithm based on LiDAR and camera information fusion[J]. Opto-Electronic Engineering, 2019, 46(7): 180420. doi: 10.12086/oee.2019.180420
Citation: Chang Xin, Chen Xiaodong, Zhang Jiachen, et al. An object detection and tracking algorithm based on LiDAR and camera information fusion[J]. Opto-Electronic Engineering, 2019, 46(7): 180420. doi: 10.12086/oee.2019.180420

基于激光雷达和相机信息融合的目标检测及跟踪

  • 基金项目:
    天津市自然科学基金项目(15JCQNJC14200)
详细信息
    作者简介:
    通讯作者: 陈晓冬(1975-),男,博士,教授,主要从事图像处理的研究。E-mail:xdchen@tju.edu.cn
  • 中图分类号: TP277; TP391

An object detection and tracking algorithm based on LiDAR and camera information fusion

  • Fund Project: Supported by Natural Science Foundation of Tianjin (15JCQNJC14200)
More Information
  • 环境感知系统是智能车辆的重要组成部分,它主要是指依赖于车载传感器对车辆周围环境进行探测。为了保证智能车辆环境感知系统的准确性和稳定性,有必要使用智能车辆车载传感器来检测和跟踪可通行区域的目标。本文提出一种基于激光雷达和摄像机信息融合的目标检测和跟踪算法,采用多传感器信息融合的方式对目标进行检测和跟踪。该算法利用激光雷达点云数据聚类方法检测可通行区域内的物体,并将其投射到图像上,以确定跟踪对象。在确定对象后,该算法利用颜色信息跟踪图像序列中的目标,由于基于图像的目标跟踪算法很容易受到光、阴影、背景干扰的影响,该算法利用激光雷达点云数据在跟踪过程中修正跟踪结果。本文采用KITTI数据集对算法进行验证和测试,结果显示,本文提出的目标检测和跟踪算法的跟踪目标平均区域重叠为83.10%,跟踪成功率为80.57%,与粒子滤波算法相比,平均区域重叠提高了29.47%,跟踪成功率提高了19.96%。

  • Overview: Intelligent vehicle refers to the new type of car which integrates a variety of technologies, including environmental perception, path planning, decision-making, controlling, etc., which carries advanced vehicle sensor, controller, actuator and other devices, can realize the car with X (people, vehicles, road, cloud, etc.) of information exchange and sharing to achieve safety, high efficiency, energy saving, and ultimately. Environmental perception is the technology which detecting vehicle environment information relies on the on-board sensors including vehicle vision sensors, LiDAR, millimeter wave radar, global positioning system (GPS), INS system and ultrasonic wave radar. In order to ensure the accuracy and stability of environmental perception of intelligent vehicle, it is necessary to use intelligent vehicle on-board sensors to detect and track the objects in the passable area. This paper puts forward a kind of object detection and tracking algorithm based on the LiDAR and camera information fusion. Firstly, this algorithm uses the LiDAR point cloud data clustering method to detect the objects in the passable area and project them onto the picture to determine the tracking objects. The LiDAR point cloud data clustering method contains filtering of original point cloud data, ground detection, passable area extraction based on point cloud data reflectivity and data clustering based on DBSCAN algorithm. After the object has been determined, this algorithm uses color information to track the object in the image sequence. Since object tracking algorithm based on image is easily influenced by light, shade and background interference, this algorithm uses LiDAR point cloud to modify tracking results in the process of tracking. The tracking strategy is: first, place N initial particles uniformly at the target position; second, calculate the similarity between the current moment particles and the previous moment particles according to the Bhattacharyya coefficient; third, resample particles according to similarity; finally, since LiDAR point cloud can be projected onto picture, calculate the object position by combining the particles and the point cloud through the algorithm. At the end of paper, this paper uses KITTI data set to test and verify the algorithm. KITTI dataset is established by Germany Karlsruhe Institute of Technology and Technology Research Institute in the United States, which is currently the largest data of computer vision algorithm for automatic driving scenarios evaluation. The experiment used a computer with 4 GB memory as the experimental platform and programmed on MATLAB 2017b. In this paper, particle filter, unscented Kalman filter (UKF) and DCO-X algorithm are used as comparison algorithms to verify the effectiveness of the algorithm. Experiments show that the algorithm has a good effect in object tracking evaluation standard of X direction, Y direction errors and center position error, regional overlap and the success rate.

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  • 图 1  目标检测流程图

    Figure 1.  Flow chart of target detection

    图 2  坐标变换流程图

    Figure 2.  Flow chart of coordinate transformation

    图 3  可通行区域提取流程图

    Figure 3.  Flow chart of passable area extraction

    图 4  目标跟踪流程图

    Figure 4.  Flow chart of object tracking

    图 5  典型DDistm曲线

    Figure 5.  Typical DDistm graph

    图 6  目标检测结果。(a), (b)点云数据的三维显示图;(c), (d)图片中的目标检测结果

    Figure 6.  Target detection results. (a), (b) 3D plot of point cloud; (c), (d) Target detection results in pictures

    图 7  目标追踪效果图

    Figure 7.  Target tracking results

    图 8  目标追踪结果。(a) X方向追踪轨迹;(b) Y方向追踪轨迹

    Figure 8.  Target tracking results. (a) X-trace; (b) Y-trace

    表 1  四种算法的性能比较

    Table 1.  Performance comparison of four algorithms

    exa/pixel eya/pixel eac/pixel oar/% Rs/%
    本文方法 1.022 3.431 3.724 83.10 80.57
    DCO-X 3.389 6.096 7.852 78.85 80.39
    UKF 5.059 9.443 12.664 66.60 71.42
    PF 23.022 11.884 27.806 56.63 60.61
    下载: 导出CSV
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出版历程
收稿日期:  2018-08-04
修回日期:  2018-09-06
刊出日期:  2019-07-01

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