Random Finite Set-Based Anomaly Detection for Safety Monitoring in Construction Sites

Low visibility hazard detection in construction sites is a crucial task for prevention of fatal accidents. Manual monitoring of construction workers to ensure they follow the safety rules (e.g., wear high-visibility vests) is a cumbersome task and practically infeasible in many applications. Therefore, an automated monitoring system is of both fundamental and practical interest. This paper proposes an intelligent solution that uses live camera images to detect workers who breach safety rules by not wearing high-visibility vests. The proposed solution is formulated in the form of an anomaly detection algorithm developed in the random finite set (RFS) framework. The proposed system is comprised of three steps: 1) applying a deep neural network to extract people in the image; 2) extracting particularly engineered features from each blob returned by the deep neural network; and 3) applying the RFS-based anomaly detection algorithm to each set of detected features. The experimental results demonstrate that in terms of F1-score, the proposed solution (as the combination of the newly engineered features and RFS-based anomaly detection algorithm) significantly outperforms various combinations of common and the state-of-the-art features and anomaly detection algorithms employed in machine vision applications.