Self-Updating Vehicle Monitoring Framework Employing Distributed Acoustic Sensing towards Real-World Settings

• URL: http://arxiv.org/abs/2409.10259v1
• Date: Mon, 16 Sep 2024 13:10:58 GMT
• Title: Self-Updating Vehicle Monitoring Framework Employing Distributed Acoustic Sensing towards Real-World Settings
• Authors: Xi Wang, Xin Liu, Songming Zhu, Zhanwen Li, Lina Gao,
• Abstract summary: We introduce a real-time semi-supervised vehicle monitoring framework tailored to urban settings. It requires only a small fraction of manual labels for initial training and exploits unlabeled data for model improvement. We propose a novel prior loss that incorporates the shapes of vehicular traces to track a single vehicle with varying speeds.
• Score: 5.306938463648908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent emergence of Distributed Acoustic Sensing (DAS) technology has facilitated the effective capture of traffic-induced seismic data. The traffic-induced seismic wave is a prominent contributor to urban vibrations and contain crucial information to advance urban exploration and governance. However, identifying vehicular movements within massive noisy data poses a significant challenge. In this study, we introduce a real-time semi-supervised vehicle monitoring framework tailored to urban settings. It requires only a small fraction of manual labels for initial training and exploits unlabeled data for model improvement. Additionally, the framework can autonomously adapt to newly collected unlabeled data. Before DAS data undergo object detection as two-dimensional images to preserve spatial information, we leveraged comprehensive one-dimensional signal preprocessing to mitigate noise. Furthermore, we propose a novel prior loss that incorporates the shapes of vehicular traces to track a single vehicle with varying speeds. To evaluate our model, we conducted experiments with seismic data from the Stanford 2 DAS Array. The results showed that our model outperformed the baseline model Efficient Teacher and its supervised counterpart, YOLO (You Only Look Once), in both accuracy and robustness. With only 35 labeled images, our model surpassed YOLO's mAP 0.5:0.95 criterion by 18% and showed a 7% increase over Efficient Teacher. We conducted comparative experiments with multiple update strategies for self-updating and identified an optimal approach. This approach surpasses the performance of non-overfitting training conducted with all data in a single pass.

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