As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.

中文翻译：

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基于变更点检测算法的基于计算机视觉的交通监控多区域分析

由于深度学习的显着进步，计算机视觉技术已在交通监控领域得到广泛采用。但是，很难找到一种可以测量交通参数的通用模型，而与环境条件（例如一天中的时间，天气或阴影）无关。这些条件经常发生变化，但是变化的确切点并不一致且不可预测。因此，即使预先准备了单独的模型参数集，多区域方法的应用也会出现问题。在本研究中，我们设计了一种强大的方法来促进多领域分析。这种方法采用在线参数算法来确定环境条件的变化点。使用自动编码器来缩小输入图像的尺寸，并使用简化的特征向量来实现在线变更点算法。在给定的一天中，将七个不同的时期标记为典型时间。然后进行多区域分析，以便可以分别测量每个时期的交通密度。为了训练和测试用于车辆计数的模型，每个时期随机选择1,100幅视频图像并贴上流量计数。多区域分析的测量精度远高于对所有数据进行训练的集成模型的测量精度。每个时段随机选择100张视频图像，并用流量计数标记。多区域分析的测量精度远高于对所有数据进行训练的集成模型的测量精度。每个时段随机选择100张视频图像，并用流量计数标记。多区域分析的测量精度远高于对所有数据进行训练的集成模型的测量精度。