  Along with the development of modern smart cities, human-centric video
analysis has been encountering the challenge of analyzing diverse and complex
events in real scenes. A complex event relates to dense crowds, anomalous
individuals, or collective behaviors. However, limited by the scale and
coverage of existing video datasets, few human analysis approaches have
reported their performances on such complex events. To this end, we present a
new large-scale dataset with comprehensive annotations, named Human-in-Events
or HiEve (Human-centric video analysis in complex Events), for the
understanding of human motions, poses, and actions in a variety of realistic
events, especially in crowd & complex events. It contains a record number of
poses (>1M), the largest number of action instances (>56k) under complex
events, as well as one of the largest numbers of trajectories lasting for
longer time (with an average trajectory length of >480 frames). Based on its
diverse annotation, we present two simple baselines for action recognition and
pose estimation, respectively. They leverage cross-label information during
training to enhance the feature learning in corresponding visual tasks.
Experiments show that they could boost the performance of existing action
recognition and pose estimation pipelines. More importantly, they prove the
widely ranged annotations in HiEve can improve various video tasks.
Furthermore, we conduct extensive experiments to benchmark recent video
analysis approaches together with our baseline methods, demonstrating HiEve is
a challenging dataset for human-centric video analysis. We expect that the
dataset will advance the development of cutting-edge techniques in
human-centric analysis and the understanding of complex events. The dataset is
available at http://humaninevents.org
