A Tracking Method of Multi-Workers Onsite with Kalman Filter and OpenPose
Publication: ICCREM 2021
ABSTRACT
Monitoring onsite construction workers are crucial for safety inspection and project management. However, tracking multi-workers’ continuous dynamic trajectories and behaviors are still full of challenge in computer vision research field. Moreover, unlike automatic unmanned or pedestrian tracking fields, construction onsite tracking lacks labeled surveilling data sets, which leads to low-effective coherent benchmarks for training neural network models. To solve above problems, this paper proposed an associate-method with Kalman filter and OpenPose model to get trajectories, behaviors, and workers’ ID together simultaneously. This method can keep tracking correct ID in complex onsite environment, reduce manual labeling workload, and enhance the preprocessing speed of original onsite videos for tracking datasets production.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abduallah, K. Q., Mohamed, E., and Christian, C. (2020). “Social-stgcnn: a social spatio-temporal graph convolutional neural network for human trajectory prediction.” The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, 14424–14432.
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (2016). “Social lstm: human trajectory prediction in crowded spaces.” 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, U.S., 961–971.
Cai, J., Zhang, Y., Yang, L., Cai, H., and Li, S. (2020). “A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites. “ Advanced Engineering Informatics, 46(7), 34–39.
Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., and Sheikh, Y. (2019). “Openpose: realtime multi-person 2d pose estimation using part affinity fields.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–186.
Konstantinou, E. J., Lasenby, G., and Brilakis, I. (2019). “Adaptive computer vision-based 2D tracking of workers in complex environments.” Automation in Construction, 103(7), 168–184.
Lee, Y. J., and Park, M. W. (2019). “3D tracking of multiple onsite workers based on stereo vision.” Automation in Construction, 98(4), 146–159.
Osokin, D. (2018). “Real-time 2d multi-person pose estimation on CPU: lightweight openpose.” Arxiv Preprint Arxiv, 1811, 12004.
Tang, S., Golparvar-Fard, M., Naphade, M., and Gopalakrishna, M. M. (2019). “Video-based activity forecasting for construction safety monitoring use cases.” ASCE International Conference on Computing in Civil Engineering, Atlanta, U.S., 204–210.
Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., and Wong, A. Y.et al. (2019). “An automatic and non-invasive physical fatigue assessment method for construction workers.” Automation in Construction, 103(6), 1–12.
Zhu, Z. H., Ren, X. N., and Chen, Z. (2017). “Integrated detection and tracking of workforce and equipment from construction jobsite videos.” Automation in Construction, 81(7), 161–171.
Information & Authors
Information
Published In
ICCREM 2021
Pages: 272 - 280
Copyright
© 2021 American Society of Civil Engineers.
History
Published online: Dec 9, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.