Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of on-road pedestrians. Most vehicles are equipped with visual sensors, however, the large-scale visual data has not been well studied yet. 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 multiple 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, to alleviate the impact of the image style variations caused by different cameras, a color transfer module is effectively incorporated to extract cross-camera consistent appearance features 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 project page is available at https://dhu-mmct.github.io/.

翻译：确保自动驾驶车辆的行驶安全日益重要，这凸显了对道路上行人进行系统化跟踪的需求。大多数车辆配备了视觉传感器，然而大规模视觉数据尚未得到充分研究。多目标多相机（MTMC）跟踪系统由单相机跟踪（SCT）和跨相机跟踪（ICT）两个模块组成。为实现两者间的可靠协调，MTMC跟踪一直是极为复杂的任务，而跨多个移动相机的跟踪更增加了挑战性。本文聚焦于多目标多移动相机（MTMMC）跟踪，这一方向正引起研究界的日益关注。针对MTMMC跟踪数据集匮乏的现状，我们收集了一个名为"多移动相机跟踪（MMCT）"的新数据集，其中包含多种驾驶场景下的序列。为解决现有大多数SCT跟踪器易出现的身份切换常见问题（尤其因移动相机与目标间的自运动导致的问题），我们提出了一种轻量级无外观全局链接模型Linker，通过将同一相机内同一目标的两个不连续轨迹片段关联为完整轨迹，从而缓解身份切换。结合Linker后，现有SCT跟踪器普遍获得显著性能提升。此外，为减轻不同相机引起的图像风格差异影响，我们有效引入了颜色迁移模块，用于提取跨相机一致的外观特征以实现移动相机间的行人关联（ICT），最终构建了性能大幅提升的MTMMC跟踪系统，这为多个移动相机的协同挖掘迈出了重要一步。项目主页详见https://dhu-mmct.github.io/。