论文标题
基于REID和相机链接模型的车辆的交通能力摄像机跟踪
Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model
论文作者
论文摘要
多目标多摄像机跟踪（MTMCT），即在多个摄像机中跟踪多个目标，是针对智能城市应用的至关重要技术。在本文中，我们为车辆提出了一个有效且可靠的MTMCT框架，该框架由流量吸引人的单相机跟踪（TSCT）算法组成，这是一种基于轨迹的摄像头链路（CLM），用于车辆重新识别（REID）和层次集群集群算法，以获得跨摄像头车辆的跨摄像头车辆。首先，提出了共同考虑车辆外观，几何特征和一些常见的交通情况的TSCT，以分别跟踪每个相机中的车辆。其次，采用基于轨迹的CLM来促进每对相邻连接的相机之间的关系，并为随后的车辆REID添加时空约束，并暂时关注。第三，分层聚类算法用于合并所有相机之间的车辆轨迹，以获得最终的MTMCT结果。我们提出的MTMCT在CityFlow数据集上进行了评估，并以74.93％的IDF1获得了新的最先进性能。
Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.
