Deep learning algorithms for person re-identification: sate-of-the-art and research challenges

Yadav, Ankit; Vishwakarma, Dinesh Kumar

doi:10.1007/s11042-023-16286-w

Cited by 5 publications

(1 citation statement)

References 170 publications

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“…Cai et al [43] used Faster R-CNN to detect the head and body orientation of construction workers and then applied a multi-task learning network to assess the direction of visual attention of construction workers. Person re-identification technology is a popular direction of computer vision in recent years [44], but the technology is not much used in the fields of engineering and construction. Cheng et al [45] combined the application of object detection methods and person re-identification algorithms for the detection of personal protective equipment based on worker tracking.…”

Section: Computer Vision Technology and Engineering Applicationsmentioning

confidence: 99%

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

Li,

Zhao,

Kong

et al. 2024

Buildings

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Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

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Section: Computer Vision Technology and Engineering Applicationsmentioning

confidence: 99%