基于跟踪算法和模糊推理的交通事件检测方法
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Freeway Incident Detection Algorithm Based on Video Tracking and Fuzzy Inference
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    摘要:

    针对高速公路交通事故容易引发大规模拥堵的问题,提出了一种基于视频的快速检测算法,实现了事件信息的快速反馈。首先利用平稳序列法获取背景,并由背景差法获取前景。然后在使用凸包占有率对遮挡进行检测的基础上,采用改进的卡尔曼滤波特征匹配跟踪算法对车辆进行跟踪。最后通过对交通流的速度和流量进行检测,建立速度、流量与交通流状态之间的映射关系,运用模糊推理方法判别交通事件的发生。实验结果证明,本文提出的 方法能有效地获取前景信息,并能实时有效地对高速公路上的交通事件进行检测。

    Abstract:

    Aiming that highway incidents are apt to cause massive congestion, a video-based rapid detection algorithm for fast feedback event information is proposed. Firstly, the smoothing sequence method is used to get the background, and the background differencing method is utilized to obtain the foreground target. Secondly, using convex hull share on detecting occlusion, the car with the modified Kalman filter feature matching algorithm is tracked. Finally, through detecting the traffic flow speed and flow, the map relationships between speed, flow and the states of traffic flow is built. The fuzzy inference method is used to detect the traffic incident. Experimental results show that the method could obtain accurate prospect information, and it is applicable and high time-efficiency for traffic incident detection on the freeway.

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朱文兴 刘晓亮 赵成龙.基于跟踪算法和模糊推理的交通事件检测方法[J].数据采集与处理,2016,31(6):1115-1126

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  • 在线发布日期: 2018-04-09