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This paper presents our solution to ACM MM challenge: Large-scaleHuman-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 hasnot been well addressed. We formulate the problem as several subproblems to besolved. First, we use a multi-object tracking method to assign human ID to eachbounding box generated by the detection model. After that, a pose is generatedto each bounding box with ID. At last, optical flow is used to take advantageof the temporal information in the videos and generate the final pose trackingresult.

A Simple Baseline for Pose Tracking in Videos of Crowded Scenes