The invention provides a regional road network vehicle path reconstruction deduction method and system based on multi-source data fusion, and the method comprises the steps: placing collected video monitoring data into a YOLO series or SSD deep learning detection model for training, and obtaining a vehicle detection model; and obtaining a vehicle detection result by adopting the vehicle detection model after iterative optimization. Then, according to the license plate number, the vehicle position and the sequence of the detection time points, a detection node information chain is constructed for the vehicle; a path reconstruction deduction data set is constructed for the detection node information chain, an objective function of the path reconstruction deduction data set is constructed, constraint conditions of the objective function are set, and a path deduction optimization model is obtained; and finally, calculating and analyzing by adopting a breadth-first search algorithm and a path deduction optimization model to obtain an optimal path of the vehicle in the road network. Compared with the prior art, the method provided by the invention solves the problem that a correct path is difficult to find during path reconstruction under a limited camera.
本发明提供了基于多源数据融合的区域路网车辆路径重构推演方法及系统,本发明方法先将采集到的视频监控数据置入YOLO系列或SSD深度学习检测模型中进行训练,得到车辆检测模型。再采用迭代优化后的车辆检测模型,获取车辆检测结果。然后,根据车牌号和车辆位置,按检测时间点的先后顺序,给车辆构建检测节点信息链。接着给检测节点信息链构建路径重构推演数据集,并构建路径重构推演数据集的目标函数,并设立目标函数的约束条件,得到路径推演优化模型。最后采用广度优先搜索算法和路径推演优化模型,计算分析得到车辆在路网中的最优路径。相较于现有技术,本发明方法解决了有限摄像头下路径重构时难以找到正确路径的难题。
Regional road network vehicle path reconstruction deduction method and system based on multi-source data fusion
基于多源数据融合的区域路网车辆路径重构推演方法及系统
2025-01-07
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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