KITTI-tracking
收藏帕依提提2024-03-04 收录
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The object tracking benchmark consists of 21 training sequences and 29 test sequences. Despite the fact that we have labeled 8 different classes, only the classes 'Car' and 'Pedestrian' are evaluated in our benchmark, as only for those classes enough instances for a comprehensive evaluation have been labeled. Our recording platform is a Volkswagen Passat B6, which has been modified with actuators for the pedals (acceleration and brake) and the steering wheel. The data is recorded using an eight core i7 computer equipped with a RAID system, running Ubuntu Linux and a real-time database. We use the following sensors: The laser scanner spins at 10 frames per second, capturing approximately 100k points per cycle. The vertical resolution of the laser scanner is 64. The cameras are mounted approximately level with the ground plane. The camera images are cropped to a size of 1382 x 512 pixels using libdc's format 7 mode. After rectification, the images get slightly smaller. The cameras are triggered at 10 frames per second by the laser scanner (when facing forward) with shutter time adjusted dynamically (maximum shutter time: 2 ms). Our sensor setup with respect to the vehicle is illustrated in the following figure. Note that more information on calibration parameters is given in the calibration files and the development kit (see raw data section).
本目标跟踪基准数据集(object tracking benchmark)包含21个训练序列与29个测试序列。尽管我们已标注8个不同类别,但本基准仅对轿车(Car)和行人(Pedestrian)两类进行评估——因仅这两类具备足够多的标注样本以支撑全面评测。本次数据采集平台为改装后的大众帕萨特B6(Volkswagen Passat B6),改装内容包括为油门、刹车踏板及方向盘加装执行机构。数据采集工作由搭载独立磁盘冗余阵列(RAID)的八核英特尔酷睿i7处理器的计算机完成,该系统运行Ubuntu Linux操作系统并配备实时数据库。我们采用的传感器配置如下:激光扫描仪以每秒10帧的转速旋转,每个扫描周期可采集约10万个点云数据,其垂直分辨率为64。相机安装高度大致与地面齐平,采集的图像经libdc格式7模式裁剪为1382×512像素尺寸,经过图像校正(rectification)后尺寸会略有缩小。相机由激光扫描仪以每秒10帧的频率触发(前向拍摄时),快门时长可动态调整(最大快门时长为2毫秒)。本传感器相对于车辆的安装配置如下图所示。有关标定参数的更多细节,请参见标定文件与开发工具包(详见原始数据章节)。
提供机构:
帕依提提搜集汇总
数据集介绍

背景与挑战
背景概述
KITTI-tracking是一个用于自动驾驶领域的对象跟踪基准数据集,包含21个训练序列和29个测试序列,主要评估'Car'和'Pedestrian'两类对象的跟踪性能。数据集通过多传感器(如激光扫描仪和摄像头)收集,提供2D和3D框跟踪数据,适用于机器视觉和自动驾驶相关研究。
以上内容由遇见数据集搜集并总结生成



