A Crowd Behavior Analysis Method for Large-Scale Performances

This study combines visual and athletic information to analyze crowd performance, using performance density entropy and performance consistency as visual descriptors and group collectivity as an athletic descriptor. We used these descriptors to develop a crowd performance behavior classification algorithm that can distinguish between different behaviors in large-scale performances. The study found that the descriptors were weakly correlated, indicating that they capture different dimensions of performance. The crowd behavior classification experiments showed that the descriptors were valid for qualitative analysis and consistent with human perception. The proposed algorithm successfully differentiated and described performance behavior in the dataset of a large-scale crowd performance and was demonstrated to be effective.