论文标题
使用深卷积神经网络的交通拥堵预测：一种颜色编码方法
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach
论文作者
论文摘要
由于计算机视觉的最新进展，流量视频数据已成为限制交通拥堵状况的关键因素。这项工作提出了一种独特的技术，用于使用颜色编码方案进行交通视频分类，然后再训练深卷积神经网络中的流量数据。首先，将视频数据转换为图像数据集。然后，使用您只看一次算法进行车辆检测。已经采用了颜色编码的方案将图像数据集转换为二进制图像数据集。这些二进制图像被馈送到深度卷积神经网络中。使用UCSD数据集，我们获得了98.2％的分类精度。
The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a Deep Convolutional Neural Network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%.
