用摄像头实时检测车辆，提升交通信号调控效率
Pixels to Signals: A Real-Time Framework for Traffic Demand Estimation
- 通过帧间平均构建道路背景，再用DBSCAN聚类识别车辆
- 无需改造基础设施，计算高效适合城市大规模部署
- 为交通信号优化提供实时车辆数据，适合智慧交通系统
城市交通拥堵日益严重，导致通勤延误和运输效率下降。为缓解此问题，本文提出一种优化交通流、减少延误的综合框架，包含车辆检测、交通预测和信号灯优化三部分。本文聚焦第一部分——车辆检测。方法基于摄像头连续视频帧，通过时间平均计算道路背景，再从前景中提取车辆。采用密度聚类算法DBSCAN实现车辆识别。该方法计算高效，无需额外硬件改造，具备良好的可扩展性，适用于真实场景的大规模部署。
原文摘要 · Abstract (English)
Traffic congestion is becoming a challenge in the rapidly growing urban cities, resulting in increasing delays and inefficiencies within urban transportation systems. To address this issue a comprehensive methodology is designed to optimize traffic flow and minimize delays. The framework is structured with three primary components: (a) vehicle detection, (b) traffic prediction, and (c) traffic signal optimization. This paper presents the first component, vehicle detection. The methodology involves analyzing multiple sequential frames from a camera feed to compute the background, i.e. the underlying roadway, by averaging pixel values over time. The computed background is then utilized to extract the foreground, where the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied to detect vehicles. With its computational efficiency and minimal infrastructure modification requirements, the proposed methodology offers a practical and scalable solution for real-world deployment.
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