Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of pedestrians on the road. Most vehicles are equipped with visual sensors, however, the large-scale visual dataset from different agents has not been well studied yet. Basically, most of the multi-target multi-camera (MTMC) tracking systems are composed of two modules: single camera tracking (SCT) and inter-camera tracking (ICT). To reliably coordinate between them, MTMC tracking has been a very complicated task, while tracking across multi-moving cameras makes it even more challenging. In this paper, we focus on multi-target multi-moving camera (MTMMC) tracking, which is attracting increasing attention from the research community. Observing there are few datasets for MTMMC tracking, we collect a new dataset, called Multi-Moving Camera Track (MMCT), which contains sequences under various driving scenarios. To address the common problems of identity switch easily faced by most existing SCT trackers, especially for moving cameras due to ego-motion between the camera and targets, a lightweight appearance-free global link model, called Linker, is proposed to mitigate the identity switch by associating two disjoint tracklets of the same target into a complete trajectory within the same camera. Incorporated with Linker, existing SCT trackers generally obtain a significant improvement. Moreover, a strong baseline approach of re-identification (Re-ID) is effectively incorporated to extract robust appearance features under varying surroundings for pedestrian association across moving cameras for ICT, resulting in a much improved MTMMC tracking system, which can constitute a step further towards coordinated mining of multiple moving cameras. The dataset is available at https://github.com/dhu-mmct/DHU-MMCT}{https://github.com/dhu-mmct/DHU-MMCT .

翻译：确保自动驾驶车辆的行驶安全日益重要，这凸显了对道路行人进行系统性跟踪的需求。大多数车辆配备了视觉传感器，但来自不同智能体的大规模视觉数据集尚未得到充分研究。多目标多相机（MTMC）跟踪系统通常由两个模块组成：单相机跟踪（SCT）和相机间跟踪（ICT）。为可靠协调这两个模块，MTMC跟踪已成为一项极其复杂的任务，而跨运动相机跟踪则进一步增加了难度。本文聚焦于多目标多运动相机（MTMMC）跟踪，该方向正日益受到研究界关注。鉴于MTMMC跟踪数据集匮乏，我们构建了一个名为多运动相机跟踪（MMCT）的新数据集，其包含多种驾驶场景下的序列。针对现有SCT跟踪器普遍存在的身份切换问题（尤其在运动相机中因相机与目标间自运动导致），我们提出了一种轻量级无外观特征的全局关联模型Linker，通过将同一目标的两个分离轨迹片段关联为完整轨迹，有效缓解身份切换问题。与Linker集成后，现有SCT跟踪器通常获得显著性能提升。此外，我们还将强基线重识别（Re-ID）方法有效融入跨运动相机的行人关联，以提取鲁棒的外观特征用于ICT，从而构建出性能大幅提升的MTMMC跟踪系统，这为多运动相机协同挖掘迈出了重要一步。数据集可从https://github.com/dhu-mmct/DHU-MMCT获取。