Auditorium Graduation Ceremony Human Pose Detection Video

In the current fields of video surveillance and behavior recognition, accurately detecting and analyzing human poses is a key challenge. Existing pose detection technologies perform poorly in complex backgrounds and multi-person environments, and require high real-time performance. This dataset aims to address the need for accurately detecting and recognizing various human poses and behaviors in auditoriums, schools, and other scenarios. The dataset was collected during real graduation ceremonies using high-definition camera equipment to ensure video quality. To improve the accuracy of annotations, multiple rounds of annotation and consistency checks have been conducted, followed by expert team review. The annotation team consists of 10 experts in the fields of computer vision and behavior analysis. Data preprocessing includes video clipping, frame alignment, and noise filtering, with storage in a frame-by-frame manner for analysis convenience.