Reference and Target Datasets with the Point-Descriptor-Precedence representation

There are four reference datasets generated using the animations given in slides 1-4 of the Animation.pptx: Reference Movement Pattern with one-point, two-points, four-points, and two-points for the length, width and speed analysis. The animations are stored in the video format and then a sequence of images were extracted to create static reference datasets/movement patterns. The Target dataset consists of 46 vehicles and is created using the animation given in Target.pptm, which has a built in macro to run the simulation and save it in a video format. The video is then used to extract images which matched exactly with the reference datasets. This resulted in finding the exact matches of the reference datasets. The code is written in Python and can be run with any Python IDE.本数据集包含四组参考数据集，均基于Animation.pptx演示文稿第1至4页内的动画生成，涵盖单点、两点、四点，以及用于长宽与速度分析的两点共四种参考运动模式。所有动画先以视频格式存储，随后通过提取图像序列构建静态参考数据集与运动模式。 目标数据集（Target dataset）共包含46个车辆模型，基于Target.pptm演示文稿内的动画创建。该演示文稿内置宏程序以运行仿真并将仿真结果保存为视频格式，随后从该视频中提取与参考数据集完全匹配的图像帧，最终实现参考数据集的精准匹配。 本数据集配套的代码采用Python编写，可在任意Python集成开发环境（IDE）中运行。