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Multi-Worker Tracking Algorithm Based on Combined Feature Clustering
The tracking of workers on construction sites is essential for safety management and resource management. However, existing vision-based worker tracking algorithms perform poorly in multi-target tracking and occlusion scenarios. A multi-worker tracking algorithm based on combined feature clustering is proposed in this study to fill the gap. Combined features are first extracted from construction scenarios to represent the targets—workers; then, feature screening and clustering are conducted to track worker targets. The algorithm is tested in a simulated scenario; the experiment result shows that the algorithm can detect and identify workers within 50 ms, with a tracking accuracy of 90.8%, thus being acceptable for construction management.
Multi-Worker Tracking Algorithm Based on Combined Feature Clustering
The tracking of workers on construction sites is essential for safety management and resource management. However, existing vision-based worker tracking algorithms perform poorly in multi-target tracking and occlusion scenarios. A multi-worker tracking algorithm based on combined feature clustering is proposed in this study to fill the gap. Combined features are first extracted from construction scenarios to represent the targets—workers; then, feature screening and clustering are conducted to track worker targets. The algorithm is tested in a simulated scenario; the experiment result shows that the algorithm can detect and identify workers within 50 ms, with a tracking accuracy of 90.8%, thus being acceptable for construction management.
Multi-Worker Tracking Algorithm Based on Combined Feature Clustering
Zhou, Ying (author) / Lin, Xiao (author) / Luo, Zhubang (author) / Guo, Hongling (author)
International Conference on Construction and Real Estate Management 2022 ; 2022 ; Virtual Conference
ICCREM 2022 ; 276-283
2022-12-15
Conference paper
Electronic Resource
English
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