SpatioTemporal Graph Dataset for Exposing Perspective and Occlusion Faults in Video Surveillance

This repository hosts a highly controlled, 18-scenario video anomaly detection benchmarking dataset comprising 7,400 high-definition frames designed to isolate and evaluate the physical and geometric failure boundaries of 2D spatio-temporal graph neural networks. While contemporary skeleton-based surveillance tracking models operate as privacy-preserving solutions by tokenizing human actions into abstract coordinate graphs, standard evaluation databases are heavily bottlenecked by uncalibrated web-scraped feeds. Shifting camera pitches, dynamic auto-exposures, and unstable frame rates introduce compounding spatial variables that mask the underlying algorithmic limitations of coordinate-based architectures. To mathematically isolate these elements, this dataset was engineered within a standardized 12 by 8 meter laboratory footprint under uniform, static fluorescent lighting. Visual sequences were recorded utilizing a fixed-position, ceiling-mounted RGB surveillance sensor locked at a resolution of 1920 by 1080 pixels at exactly 30.0 frames per second. The tracking canvas is marked with metric ground-truth boundaries extending along the primary optical axis from Z = 0.5 meters to Z = 6.0 meters, allowing direct quantification of perspective coordinate degradation.