Vehicle visual tracking is a challenging issue in intelligent transportation systems. The tracking gets more challenging when vehicles change direction at intersections. Undetermined motion flows, occlusion, and congestion are the potential issues of vehicle tracking at intersections. In this study, a new method for tracking multiple vehicles from a multi-view is proposed to overcome occlusion caused at the intersections with undetermined motion flows. In the authors’ method, a multilayer graph is presented that assigns motion flows to distinct layers with different neighbourhoods for each layer represented by the graph's edges. Hence, the vehicle trajectories are distributed among layers such that vehicles entering from the same side with similar motion flows are assigned to the same layer. All multilayer graphs of different views are mapped to the graph of the selected view. Then, tracking is performed on the distinct layers of the mapped multilayer graph by computing min-cost flows. In cases such as vehicle crossing, misdetection, or occlusion, the method can predict the vehicle's tracks by using history, layer neighbourhoods, and other views’ information. Experimental results show a consistency of the ground truth and the analysis obtained using the proposed method in tracking vehicles in the inner part of the intersection.

中文翻译：

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基于多层图的城市交叉口多摄像机多车辆跟踪

在智能交通系统中，车辆视觉跟踪是一个具有挑战性的问题。当车辆在十字路口改变方向时，跟踪变得更具挑战性。不确定的运动流，阻塞和拥堵是交叉路口车辆跟踪的潜在问题。在这项研究中，提出了一种从多角度跟踪多辆车辆的新方法，以克服运动流不确定的交叉路口造成的遮挡。在作者的方法中，提出了一种多层图形，该图形将运动流分配给具有不同邻域的不同层，每个层由图形的边缘表示。因此，车辆轨迹在各层之间分布，使得以相同运动流从同一侧进入的车辆被分配给同一层。不同视图的所有多层图形都映射到所选视图的图形。然后，通过计算最小成本流对映射的多层图的不同层执行跟踪。在诸如车辆穿越，误检测或遮挡的情况下，该方法可以通过使用历史记录，图层邻域和其他视图的信息来预测车辆的行驶轨迹。实验结果表明了地面真实性的一致性，并使用该方法对交叉路口内部的车辆进行了跟踪分析。和其他视图的信息。实验结果表明了地面真实性的一致性，并使用该方法对交叉路口内部的车辆进行了跟踪分析。和其他视图的信息。实验结果表明了地面真实性的一致性，并使用该方法对交叉路口内部的车辆进行了跟踪分析。