Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914001315692544 |
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| author | Cohen, Khen Hen, Liav Lellouch, Ariel |
| author_facet | Cohen, Khen Hen, Liav Lellouch, Ariel |
| contents | Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classification neural network utilizing DAS data only. Our model achieves a performance exceeding 94% for detection and classification, and about 1.2% false alarm rate. We illustrate the model's application in monitoring traffic over a week, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors with visual information, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12743 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs Cohen, Khen Hen, Liav Lellouch, Ariel Geophysics Computer Vision and Pattern Recognition Machine Learning Signal Processing Optics Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classification neural network utilizing DAS data only. Our model achieves a performance exceeding 94% for detection and classification, and about 1.2% false alarm rate. We illustrate the model's application in monitoring traffic over a week, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors with visual information, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our dataset. |
| title | Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs |
| topic | Geophysics Computer Vision and Pattern Recognition Machine Learning Signal Processing Optics |
| url | https://arxiv.org/abs/2412.12743 |