Multi-target multi-camera tracking (MTMCT) of vehicles, i.e. tracking vehicles across multiple cameras, is a crucial application for the development of smart city and intelligent traffic system. The main challenges of MTMCT of vehicles include the intra-class variability of the same vehicle and inter-class similarity between different vehicles and how to associate the same vehicle accurately across different cameras under large search space. Previous methods for MTMCT usually use hierarchical clustering of trajectories to conduct cross camera association. However, the search space can be large and does not take spatial and temporal information into consideration. In this paper, we proposed a transformer-based camera link model with spatial and temporal filtering to conduct cross camera tracking. Achieving 73.68% IDF1 on the Nvidia Cityflow V2 dataset test set, showing the effectiveness of our camera link model on multi-target multi-camera tracking.

翻译：多目标多镜头跟踪车辆,即通过多摄像头跟踪车辆,是开发智能城市和智能交通系统的关键应用,机动车辆MTMCT的主要挑战包括同一车辆的等级内变异和不同车辆的等级间相似性,以及如何在大型搜索空间将同一车辆精确地连接到不同摄像头中。MTMCT以往的方法通常使用轨道分级组合进行跨相机联系。然而,搜索空间可能很大,不考虑空间和时间信息。在本文件中,我们提议采用基于变压器的相机链接模型,带有空间和时间过滤器,以进行交叉相机跟踪。Nvidia市流V2数据集实现了73.68%的以色列国防军1,展示了我们多目标多镜头跟踪的相机链接模型的有效性。