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Ensuring driving safety for autonomous vehicles has become increasinglycrucial, highlighting the need for systematic tracking of on-road pedestrians.Most vehicles are equipped with visual sensors, however, the large-scale visualdata has not been well studied yet. Multi-target multi-camera (MTMC) trackingsystems are composed of two modules: single-camera tracking (SCT) andinter-camera tracking (ICT). To reliably coordinate between them, MTMC trackinghas been a very complicated task, while tracking across multiple moving camerasmakes it even more challenging. In this paper, we focus on multi-targetmulti-moving-camera (MTMMC) tracking, which is attracting increasing attentionfrom the research community. Observing there are few datasets for MTMMCtracking, we collect a new dataset, called Multi-Moving-Camera Track (MMCT),which contains sequences under various driving scenarios. To address the commonproblems 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 tomitigate the identity switch by associating two disjoint tracklets of the sametarget into a complete trajectory within the same camera. Incorporated withLinker, existing SCT trackers generally obtain a significant improvement.Moreover, to alleviate the impact of the image style variations caused bydifferent cameras, a color transfer module is effectively incorporated toextract cross-camera consistent appearance features for pedestrian associationacross moving cameras for ICT, resulting in a much improved MTMMC trackingsystem, which can constitute a step further towards coordinated mining ofmultiple moving cameras. The project page is available athttps://dhu-mmct.github.io/.

Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model