arXiv 2509.046242025-09-04cs.CVcs.ETcs.ROcs.SYeess.IVeess.SY

基于无人机的智能交通监控系统：实时车辆检测、分类、跟踪与行为分析

UAV-Based Intelligent Traffic Surveillance System: Real-Time Vehicle Detection, Classification, Tracking, and Behavioral Analysis

Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学电气与计算机工程学院）；Transportation Research Institute (IMOB), UHasselt-Hasselt University（乌赫斯泰特大学交通研究所）；Faculty of Mechanical Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学机械工程学院）；Department of Computer Engineering, Amirkabir University of Technology（阿姆irkabir技术大学计算机工程系）

Ali Khanpour, Tianyi Wang, Afra Vahidi-Shams, Wim Ectors, Farzam Nakhaie, Amirhossein Taheri, Christian Claudel

备注：: 15 pages, 8 figures, 2 tables

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AI总结：

针对传统交通监控覆盖有限、适应性差等问题，提出基于无人机的智能交通监控系统，融合多尺度模板匹配、卡尔曼滤波等技术，实现车辆检测分类跟踪与违规行为分析，实验验证了其高精度与实用价值，可为智慧城市提供无基础设施依赖的交通监控方案。

摘要：

交通拥堵与交通违规给城市出行和道路安全带来了严峻挑战。传统交通监控系统（如固定摄像头和基于传感器的方法）往往受限于覆盖范围有限、适应性差、可扩展性不足等问题。为应对这些挑战，本文提出了一种先进的基于无人机（UAV）的交通监控系统，能够在真实、无约束的城市环境中完成精准的车辆检测、分类、跟踪与行为分析。该系统利用多尺度多角度模板匹配、卡尔曼滤波以及基于单应性的标定技术，处理从约200米高度采集的航拍视频数据。在城区开展的案例研究验证了系统的鲁棒性能：检测精度达91.8%，F1值为90.5%，跟踪指标MOTA和MOTP分别为92.1%和93.7%。除精准检测外，系统可区分5类车辆，并通过融合地理围栏、运动滤波和轨迹偏差分析，自动识别不安全变道、违法双排停车、人行横道占道等重点交通违法行为。集成分析模块支持起讫点跟踪、车流量可视化、车型间关联分析以及基于热力图的拥堵建模。此外，系统还支持进出轨迹分析、路段车辆密度估算和行驶方向记录，可支撑全面的多尺度城市出行分析。实验结果证实了系统的可扩展性、准确性和实用价值，凸显了其作为面向下一代智慧城市、具备执法感知能力、无需依赖基础设施的交通监控解决方案的潜力。

英文摘要：

Traffic congestion and violations pose significant challenges for urban mobility and road safety. Traditional traffic monitoring systems, such as fixed cameras and sensor-based methods, are often constrained by limited coverage, low adaptability, and poor scalability. To address these challenges, this paper introduces an advanced unmanned aerial vehicle (UAV)-based traffic surveillance system capable of accurate vehicle detection, classification, tracking, and behavioral analysis in real-world, unconstrained urban environments. The system leverages multi-scale and multi-angle template matching, Kalman filtering, and homography-based calibration to process aerial video data collected from altitudes of approximately 200 meters. A case study in urban area demonstrates robust performance, achieving a detection precision of 91.8%, an F1-score of 90.5%, and tracking metrics (MOTA/MOTP) of 92.1% and 93.7%, respectively. Beyond precise detection, the system classifies five vehicle types and automatically detects critical traffic violations, including unsafe lane changes, illegal double parking, and crosswalk obstructions, through the fusion of geofencing, motion filtering, and trajectory deviation analysis. The integrated analytics module supports origin-destination tracking, vehicle count visualization, inter-class correlation analysis, and heatmap-based congestion modeling. Additionally, the system enables entry-exit trajectory profiling, vehicle density estimation across road segments, and movement direction logging, supporting comprehensive multi-scale urban mobility analytics. Experimental results confirms the system's scalability, accuracy, and practical relevance, highlighting its potential as an enforcement-aware, infrastructure-independent traffic monitoring solution for next-generation smart cities.

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