In this grand challenge, we focus on very challenging and realistic tasks of human-centric analysis
in various crowd & complex events, including subway getting on/off, collision, fighting, and earthquake
escape (cf. Figure. 1). To the best of our knowledge, few existing human analysis approaches report
their performance under such complex events. With this consideration, we further propose a dataset (named as Human-in-Events or HiEve) with
large-scale and densely-annotated labels covering a wide range of tasks in human-centric analysis.
Four challenging tracks are established on our dataset:
• Track-1: Multi-person Motion Tracking in Complex Events
• Track-2: Crowd Pose Estimation in Complex Events
• Track-3: Crowd Pose Tracking in Complex Events
• Track-4: Person-level Action Recognition in Complex Events
At the end of the Challenge, all teams will be ranked based on objective evaluation. The top-3 performing teams in each track will receive certificates and awards. At the same time, teams of high performance results are invited to submit challenge papers (4-pages) and present their solutions during the conference.