The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.

中文翻译：

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一种用于行车记录传感器的新型车辆跟踪ID切换算法。

实时车辆跟踪的主要任务是与连续帧中的对象建立关联。在跟踪过程中，车辆之间发生遮挡后，在再次跟踪车辆时会为其赋予新的ID。在这项研究中，构造了一种在视频帧之间跟踪车辆的新方法。该方法应用于行车记录仪传感器。YOLO v3对神经网络模型进行了训练，该系统使用行车记录仪（DDR）收集道路上车辆的视频。我们使用带有Kalman滤波器的改进的Deep SORT算法来预测车辆的位置并计算马哈拉诺比斯，余弦和欧几里得距离。外观度量已合并到余弦距离中。实验证明我们的算法可以有效地减少29个ID开关的数量。