  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/.
