In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM). Given a video sequence and the corresponding frame-by-frame vehicle detections, we first address the isolated tracklets issue from single camera tracking (SCT) by the proposed traffic-aware single-camera tracking (TSCT). Then, after automatically constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated from camera topological configuration to obtain the spatial and temporal information to improve the performance of MTMCT by reducing the candidate search of ReID. We also use the temporal attention model to create more discriminative embeddings of trajectories from each camera to achieve robust distance measures for vehicle ReID. Moreover, we train a metadata classifier for MTMCT to obtain the metadata feature, which is concatenated with the temporal attention based embeddings. Finally, the TCLM and hierarchical clustering are jointly applied for global ID assignment. The proposed method is evaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the state-of-the-art MTMCT methods.

中文翻译：

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使用元数据辅助的 Re-ID 和基于轨迹的 Camera Link 模型对车辆进行多目标多相机跟踪

在本文中，我们提出了一种基于元数据辅助重新识别（MA-ReID）和基于轨迹的相机链接模型（TCLM）的车辆多目标多相机跟踪（MTMCT）框架。给定视频序列和相应的逐帧车辆检测，我们首先通过提出的交通感知单摄像头跟踪 (TSCT) 解决单摄像头跟踪 (SCT) 中的孤立轨迹问题。然后，在自动构建 TCLM 后，我们通过 MA-ReID 解决 MTMCT。TCLM 是根据相机拓扑配置生成的，以获取空间和时间信息，通过减少 ReID 的候选搜索来提高 MTMCT 的性能。我们还使用时间注意力模型从每个摄像头创建更具辨别力的轨迹嵌入，以实现车辆 ReID 的稳健距离测量。此外，我们为 MTMCT 训练元数据分类器以获得元数据特征，该特征与基于时间注意力的嵌入相连接。最后，TCLM 和层次聚类联合应用于全局 ID 分配。所提出的方法在 CityFlow 数据集上进行了评估，实现了 IDF1 76.77%，优于最先进的 MTMCT 方法。