Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree - enabled approach powered by Deep Learning for extracting anomalies from traffic cameras while accurately estimating the start and end time of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.
翻译:任何智能交通监测系统都必须能够实时发现交通事故等异常现象。 在本文中,我们提议了由深学习公司推动的“决定-Tree”启用方法,从交通摄像头中提取异常现象,同时准确估计异常事件的开始和结束时间。我们的方法包括建立一个检测模型,随后是异常现象的检测和分析。YOLOv5是我们检测模型的基础。异常现象的检测和分析步骤包括交通现场背景估计、道路面具提取和适应性阈值。候选人异常现象通过决策树通过检测和分析最终异常现象。根据试验验证,拟议方法得出F1分0.8571和S4分0.5686。