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

Ammar Kamoona, Amirali Khodadadian Gostar, Ruwan Tennakoon, Alireza Bab-Hadiashar, David Accadia, Joshua Thorpe, Reza Hoseinnezhad
2019 IEEE Access  
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
more » ... 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-ofthe-art features and anomaly detection algorithms employed in machine vision applications. INDEX TERMS Random finite sets, construction safety, safety monitoring, Poisson point patterns, IID clusters, PHD filter, anomaly detection. 105710 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 7, 2019 REZA HOSEINNEZHAD received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from the University of Tehran, Iran, in 1994Iran, in , 1996Iran, in , and 2002. He has held various positions at the University of Tehran, the Swinburne University of Technology, The University of Melbourne, and RMIT University, where he has worked, since 2010, and is currently a Professor, and a Research Development Lead, as well as the Discipline Leader (Manufacturing and Mechatronics) with the School of Engineering. His main research interests include statistical information fusion, random finite sets, multi-object tracking, deep learning, and robust multi-structure data fitting in computer vision.
doi:10.1109/access.2019.2932137 fatcat:grnmmdtmsrf73iteds73z2cnoy