  This paper presents our solution to ACM MM challenge: Large-scale
Human-centric Video Analysis in Complex Events\cite{lin2020human};
specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.
Remarkable progress has been made in multi-pose training in recent years.
However, how to track the human pose in crowded and complex environments has
not been well addressed. We formulate the problem as several subproblems to be
solved. First, we use a multi-object tracking method to assign human ID to each
bounding box generated by the detection model. After that, a pose is generated
to each bounding box with ID. At last, optical flow is used to take advantage
of the temporal information in the videos and generate the final pose tracking
result.
