Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China.
Comput Intell Neurosci. 2023 Feb 14;2023:3677387. doi: 10.1155/2023/3677387. eCollection 2023.
Vehicles transporting hazardous material (HAZMAT) pose a severe threat to highway safety, especially in road tunnels. Vehicle reidentification is essential for identifying and warning abnormal states of HAZMAT vehicles in road tunnels. However, there is still no public dataset for benchmarking this task. To this end, this work releases a real-world tunnel HAZMAT vehicle reidentification dataset, VisInt-THV-ReID, including 10,048 images with 865 HAZMAT vehicles and their spatiotemporal information. A method based on multimodal information fusion is proposed to realize vehicle reidentification by fusing vehicle appearance and spatiotemporal information. We design a spatiotemporal similarity determination method for vehicles based on the spatiotemporal law of vehicles in tunnels. Compared with other reidentification methods based on multimodal information fusion, i.e., PROVID, Visual + ST, and Siamese-CNN, experimental results show that our approach significantly improves the vehicle reidentification recognition precision.
运输危险材料（HAZMAT）的车辆对公路安全构成严重威胁，尤其是在道路隧道中。车辆再识别对于识别和警告道路隧道中 HAZMAT 车辆的异常状态至关重要。然而，目前尚无用于基准测试此任务的公共数据集。为此，这项工作发布了一个真实的隧道 HAZMAT 车辆再识别数据集 VisInt-THV-ReID，其中包括 10048 张包含 865 辆 HAZMAT 车辆及其时空信息的图像。提出了一种基于多模态信息融合的方法，通过融合车辆外观和时空信息来实现车辆再识别。我们设计了一种基于车辆在隧道中时空规律的车辆时空相似性确定方法。与其他基于多模态信息融合的再识别方法（如 PROVID、Visual+ST 和 Siamese-CNN）相比，实验结果表明，我们的方法显著提高了车辆再识别的识别精度。
