Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach
Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach
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DOI：
10.1177/03611981221076426
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发表时间：
2022-03
影响因子：
1.7
通讯作者：
J. Jiao;Huihai Wang
中科院分区：
文献类型：
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作者：
J. Jiao;Huihai Wang
十字路口的实时交通数据对自适应交通灯控制系统的发展具有重要意义。红外辐射和GPS等传感器无法提供详细的交通信息。与这些传感器相比，监控摄像头有可能为交通分析提供真实场景。在本研究中，采用基于YOLO （You Only Look Once）的算法从交通视频中检测和跟踪车辆，并使用预定义的道路掩码来确定不同道路上的交通流和转弯事件。在背景遮挡条件下，利用卡尔曼滤波估计和预测车辆的速度和位置。结果表明，该算法能够在均方根误差（RMSE）为10的范围内识别交通流和转向事件。结果表明，在背景遮挡的情况下，卡尔曼滤波与联合交（IOU）跟踪器具有良好的效果。此外，该算法还可以在不同的光学条件下检测和跟踪车辆。在远离交通摄像头的地区，恶劣的天气和夜间会影响检测和跟踪过程。从交通视频中提取的交通流中包含道路信息，不仅可以帮助单个交叉口的控制，还可以为路网提供信息。观测到的交通流的时间特征为基于检测到的交通流预测提供了可能，这将使交通灯控制更加有效。
Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.