论文标题
使用更改点检测算法的基于计算机视觉的流量监视的多政权分析
Multi-regime analysis for computer vision-based traffic surveillance using a change-point detection algorithm
论文作者
论文摘要
由于深度学习的重大进展，计算机视觉技术在交通监视领域已被广泛采用。尽管如此，无论环境条件（例如一天的时代，天气或阴影），很难找到可以测量流量参数的通用模型。这些条件反复差异，但是变化的确切点是不一致且不可预测的。因此，即使在预先准备好单独的模型参数时，多功能方法的应用也将是有问题的。在本研究中，我们设计了一种强大的方法，可促进多政权分析。该方法采用在线参数算法来确定环境条件的变更点。使用自动编码器来减少输入图像的尺寸，并使用简化的特征向量来实现在线更改点算法。在给定的一天中，有七个单独的时期用典型的时间标记。然后进行多政权分析，以便可以在每个时期分别测量交通密度。为了训练和测试车辆计数，为每个时期随机选择了1,100张视频图像，并用交通计数标记。多政权分析的测量精度远高于对所有数据训练的集成模型的测量精度。
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.
