PAVEMENT: Passing Vehicle Detection System with Autonomous Incremental Learning using Camera and Vibration Data
PAVEMENT: Passing Vehicle Detection System with Autonomous Incremental Learning using Camera and Vibration Data
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DOI：
10.1109/vtc2022-fall57202.2022.10012958
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发表时间：
2022-09
期刊：
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通讯作者：
Arnan Maipradit;Yumiko Moriyama;Tomoki Okuro;Makoto Yoshida;Nobuya Tachimori;Sinya Akiyama;H. Suwa;K. Yasumoto
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作者：
Arnan Maipradit;Yumiko Moriyama;Tomoki Okuro;Makoto Yoshida;Nobuya Tachimori;Sinya Akiyama;H. Suwa;K. Yasumoto
检测通过道路的车辆的系统在 ITS（智能交通系统）中发挥着重要作用，因为它们广泛适用于道路建设/维修规划、拥堵和预测的交通监控和分析。在使用摄像头、多普勒传感器等的各种系统中，利用道路振动来检测过往车辆的系统很有前景，因为它在天气条件和部署/运营成本方面具有优势。然而，为训练模型准备真实标签需要耗费大量人力。在本文中，我们提出了 PAVMENT，这是一种新型的基于自主增量学习的交通普查传感器系统，使用压电振动传感器和摄像机，无需人工干预。 PAVMENT 包含两个模型：基于视频的模型，使用边界框（由 YOLOv3 和 DeepSORT 检测）来检测车辆；基于振动的模型，使用道路振动来检测过往车辆。为了减轻收集地面实况标签的负担，我们应用线性判别分析和增量学习，通过使用基于视频的模型的结果作为地面实况来训练基于振动的模型。一旦基于振动的模型经过训练，它就可以用于在各种条件（天气、照明和其他环境因素）下无需摄像机的道路上进行交通普查。我们收集了不同地方道路上4000多辆过往车辆的视频和振动数据，并将我们的方法应用到这些数据上。结果，PAVMENT 使用在 1 分钟间隔内经过 15 个增量学习步骤训练的模型，在检测过往车辆方面实现了超过 98.4% 的准确率和 98.0% 的 f1 分数。
Systems that detect vehicles passing through roads play a significant role in ITS (Intelligence Transport Systems), due to their wide applicability to traffic monitoring and analysis for road construction/repair planning, congestion, and prediction. Among various systems using cameras, doppler sensors etc., a system that uses road vibration to detect passing vehicles is promising since it has advantages in terms of weather conditions and deployment/operation costs. However, it suffers from the human labor to prepare ground truth labels for training models. In this paper, we propose PAVEMENT, a novel Autonomous Incremental Learning based traffic-census sensor system using a piezoelectric vibration sensor and a video camera without human intervention. PAVEMENT consists of two models: the video-based model which detects vehicles by using bounding boxes (detected by YOLOv3 and DeepSORT) and the vibration-based model which uses road vibrations to detect passing vehicles. To reduce the burden of collecting ground truth labels, we apply linear discriminant analysis and incremental learning to train the vibration-based model by using the result of the video-based model as ground truth. Once the vibration-based model is trained, it can be used for traffic census on roads without the video camera for various conditions (weather, lighting, and other environmental factors). We collected the video and vibration data of more than 4,000 passing vehicles on roads in different places and applied our method to the data. As a result, PAVEMENT achieved over 98.4% accuracy and 98.0% f1-score in detecting passing vehicles using the model trained with 15 incremental learning steps in 1 minute interval.