Hashing-Based Object Tracking for Construction Site Safety Monitoring across Different Domains
Publication: Computing in Civil Engineering 2023
ABSTRACT
Construction safety monitoring increasingly relies on machine learning-based models due to their strong learning capability. However, these models’ performance often degrades when they encounter shifts in data distribution. To address this issue, a tracking algorithm can be used for the detection model to enhance detection performance and ensure high-quality safety monitoring. Most importantly, utilizing such method can lead to robust personalized workplace risk assessment and customized safety training to workers. This paper proposes a novel object tracking approach, called hashing-supported cascaded buffered intersection over union (HC-BIoU) tracking, which addresses low tracking performance due to visual domain shifting. The experimental results demonstrate that the proposed method achieves significant improvements in mean average precision (9.3%) and association accuracy (42.6%) compared to a YoloV8 object detector and a Cascaded Buffered IoU (C-BIoU) tracker, tested on a video sequence of a scaffold dismantlement scene with 1,983 frames and 9,606 ground truth bounding boxes.
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REFERENCES
Bernardin, K., and Stiefelhagen, R. (2008). “Evaluatingmultipleobjecttracking performance: the clear mot metrics.” EURASIP Journal on Image and Video Processing, 1–10.
Cheng, J. P., Wong, P. K.-Y., Luo, H., Wang, M., and Leung, P. H. (2022). “Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification.” Automation in Construction, 139, 104312.
Chern, W.-C., Hyeon, J., Nguyen, T. V., Asari, V. K., and Kim, H. (2023). “Context-aware safety assessment system for far-field monitoring.” Automation in Construction, 149, 104779.
Chern, W., Nguyen, T. V., Asari, V. K., and Kim, H. (2023). “Impact of loss functions on semantic segmentation in far‐field monitoring.” Computer-Aided Civil and Infrastructure Engineering, 38(3), 372–390.
Hong, Y., Chern, W.-C., Nguyen, T. V., Cai, H., and Kim, H. (2023). “Semi-supervised domain adaptation for segmentation models on different monitoring settings.” Automation in Construction, 149, 104773.
Jocher, G., Chaurasia, A., and Qiu, J. (2023). “YOLO by Ultralytics (Version 8.0.0).” [Computer software], https://github.com/ultralytics/ultralytics, Accessed: March 15, 2023.
Kalman, R. E. (1960). “A New Approach to Linear Filtering and Prediction Problems.” Transactions of the ASME--Journal of Basic Engineering 82: 35–45.
Kim, J., and Chi, S. (2021). “A few-shot learning approach for database-free vision-based monitoring on construction sites.” Automation in Construction, 124, 103566.
Luiten, J., Os̆ep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taixé, L., and Leibe, B. (2021). “HOTA: A Higher Order Metric for Evaluating Multi-object Tracking.” International Journal of Computer Vision, 129(2), 548–578.
Maggiolino, G., Ahmad, A., Cao, J., and Kitani, K. (2023). “Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification.” (arXiv:2302.11813). arXiv.
Mihcak, M., and Venkatesan, R. (2001). “New iterative geometric methods for robust perceptual image hashing.” In Revised Papers from the ACM CCS- 8 Workshop on Security and Privacy in Digital Rights Management, vol. 2200 of Lecture Notes in Computer Science, pp. 13–21. Springer.
Nath, N. D., Behzadan, A. H., and Paal, S. G. (2020). “Deep learning for site safety: Real-time detection of personal protective equipment.” Automation in Construction, 112, 103085.
Naticchia, B., Vaccarini, M., and Carbonari, A. (2013). “A monitoring system for real-time interference control on large construction sites.” Automation in Construction, 29, 148–160.
Park, M.-W., and Brilakis, I. (2016). “Continuous localization of construction workers via integration of detection and tracking.” Automation in Construction, 72, 129–142.
Ristani, E., Solera, F., Zou, R., Cucchiara, R., and Tomasi, C. (2016). “Per- formance measures and a data set for multi-target, multi-camera tracking”. European Conference on Computer Vision, pp. 17–35. Springer.
Wang, X., and Zhu, Z. (2021). “Vision–based framework for automatic interpretation of construction workers’ hand gestures.” Automation in Construction, 130, 103872.
Wojke, N., Bewley, A., and Paulus, D. (2017). “Simple online and realtime tracking with a deep association metric.” IEEE International Conference on Image Processing (ICIP), 3645–3649.
Yan, X., Zhang, H., and Gao, H. (2022). “Mutually coupled detection and tracking of trucks for monitoring construction material arrival delays.” Automation in Construction, 142, 104491.
Zhu, Z., Ren, X., and Chen, Z. (2017). “Integrated detection and tracking of workforce and equipment from construction jobsite videos.” Automation in Construction, 81, 161–171.
Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 500 - 507
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction management
- Construction sites
- Detection methods
- Engineering fundamentals
- Equipment and machinery
- Mathematics
- Methodology (by type)
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Tracking
Authors
Metrics & Citations
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