Joint 3D Reconstruction and Object Tracking for Traffic Video Analysis Under IoV Environment
Joint 3D Reconstruction and Object Tracking for Traffic Video Analysis Under IoV Environment
复制标题
车联网环境下交通视频分析的联合 3D 重建和目标跟踪
DOI：
10.1109/tits.2020.2995768
复制
发表时间：
2021-06-01
影响因子：
8.5
通讯作者：
Liu, Xiaoping
中科院分区：
文献类型：
--
作者：
Cao, Mingwei;Zheng, Liping;Liu, Xiaoping
得益于人工智能和车联网（IoV），现代交通管理取得了巨大进步，尤其是在城市地区。然而，传统的交通视频分析和可视化通常在非现场和纹理环境中进行，即，文本和数字，这不会促进用户的感官感知和交互。因此，如何利用现代新技术对交通视频进行分析，以提高智能交通的水平是一个迫切需要解决的问题。本文提出了一种用于车联网环境下交通视频分析的联合三维重建和目标跟踪方法，该方法是一个集成框架，由三维重建、目标检测和视觉跟踪组成。3D重建系统连接到车联网并集成到系统中，以检索用于恢复车辆3D模型的图像数据，然后通过增强现实实时可视化车辆轨迹。并且该系统还可以实时定位车辆的位置。在实验室和实践中的实验都显示了很好的反馈，这将有效地促进智能交通。
Benefits from artificial intelligence and the Internet of Vehicles (IoV), Management of modern transportation have great progress, especially in urban areas. However, traditional traffic video analysis and visualization are usually conducted in offsite and textural environments, i.e., text and number, which do not promote user’s sensorial perception and interaction. Thus, the problem that how to use modern novel techniques to analyze traffic video for improving intelligent transportation is so emergency. In this paper, we introduce a joint 3D reconstruction and object tracking approach to traffic video analysis under the IoV environment, which is an integrative framework and consists of 3D reconstruction, object detection, and visual tracking. The 3D reconstruction system is connected to the Internet of Vehicles and integrated into the system to retrieve image data for recovering the 3D model of vehicles, and then, visualizing vehicle trajectories in real-time by augmented reality. And the system can also locate the vehicle’s position in real-time. The experiments in both laboratory and practice show great feedback, which will effectively contribute to intelligent transportation.