One-Shot Traffic Analytics

在这个项目中，我们研究了监控高速公路交通以及基于道路交叉口的可能性 在单个图像上，即不使用运动特征。所提出的方法的目标是识别流量 而不是像通常情况下那样衡量它。这个项目的主要目标是让一个 用于处理无法估计其运动特征的低帧速率视频的自动系统。方法 它将利用交通场景的高度冗余性质进行开发，这些场景从 一个自上而下的视角，由车辆组成，照片中的车辆在一条主要的沥青道路上，周围是 背景对象。由于交通场景中拍摄的对象种类有限，因此我们的方法将达到 了解这些图像所特有的特征。有了这些功能，该方法将展望细分 无需精确的运动特征，即可获取交通图像、对交通场景进行分类并估计交通密度。 我们考虑使用我们最近开发的卷积神经网络(CNN)模型。从那开始 模型中，将提出不同的CNN来将交通图像分成三个不同的类别(道路、汽车和 背景)，将交通图像分类为不同类别(空、流、重、拥堵)，并预测交通 密度。

In this project, we investigate the possibility of monitoring highway traffic as well as street intersection based on a single image, i.e. without using motion features. The goal of the proposed method is to recognize traffic conditions instead of measuring it as is usually the case. The main objective of this project is to allow an automatic system to process low frame-rate videos for which motion features cannot be estimated. The method that shall be developed takes advantage of the highly redundant nature of traffic scenes which are pictured from a top-down perspective and made of vehicles pictured on a predominant asphalted road surrounded by background objects. Due to the limited variety of objects pictured in traffic scenes, our method shall get to learn features that are unique to such images. With these features, the method will look forward at segmenting traffic images, classify traffic scenes, and estimate traffic density without the need for accurate motion features. We consider using a convolutional neural network (CNN) model that we recently developed. From that model, different CNN will be proposed to segment traffic images in three different classes (road, car and background), classify traffic images into different categories (empty, fluid, heavy, jam) and predict traffic density.