One-Shot Traffic Analytics

一次性流量分析

• 批准号: 490789-2015
• 负责人: Jodoin, PierreMarc
• 金额: $ 1.82万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586613
• 关键词: Shot; Traffic; Analytics

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.

在这个项目中，我们调查的可能性，监测高速公路交通以及街道交叉口的基础上， 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 而不是像通常那样测量它。该项目的主要目标是让一个 自动系统，用于处理运动特征无法估计的低帧率视频。述的方法 应开发利用交通场景的高度冗余性， 一个自上而下的角度，并在一个主要的沥青道路周围的车辆图片 背景物体。由于交通场景中拍摄的对象种类有限，我们的方法应达到 学习这些图像独有的特征。有了这些特征，该方法将有望在分割 交通图像，分类交通场景，并估计交通密度，而不需要精确的运动特征。 我们考虑使用我们最近开发的卷积神经网络（CNN）模型。从 模型，不同的CNN将提出分割交通图像在三个不同的类（道路，汽车和 背景），将交通图像分类为不同的类别（空，流体，重，堵塞）并预测交通 密度的