Construction sites are high-risk environments where changing obstacles pose dangers of falls and collisions for workers.Traditional safety management, relying on visual inspections or wearable sensors, faces practical limitations such as equipment costs and worker resistance.To address these issues, this study proposes using existing CCTV infrastructure and video-based keypoint detection technology to detect walking abnormalities.We define the spatiotemporal correlation between the left and right ankle coordinates as a feature and compare the performance of four anomaly detection methods (Z-score, IQR, Mahalanobis Distance, LSTM Autoencoder).The results show that statistical methods often produce false positives due to a lack of temporal context, while the LSTM Autoencoder model performed best with an F1-score of 83.1%.This study demonstrates the feasibility of building an intelligent safety management system that identifies potential walking hazards through video analysis alone, without additional equipment.
Kim et al. (Mon,) studied this question.