  Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories
from videos captured by a set of cameras. Recently, the tracking performance of
MTMCT is significantly enhanced with the employment of re-identification
(Re-ID) model. However, the appearance feature usually becomes unreliable due
to the occlusion and orientation variance of the targets. Directly applying
Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and
tracklet fragment caused by occlusion. To solve these problems, we propose a
novel tracking framework in this paper. In this framework, the occlusion status
and orientation information are utilized in Re-ID model with human pose
information considered. In addition, the tracklet association using the
proposed fused tracking feature is adopted to handle the fragment problem. The
proposed tracker achieves 81.3\% IDF1 on the multiple-camera hard sequence,
which outperforms all other reference methods by a large margin.
