Detection and classification of road users for video surveillance applications

这个项目的目标是设计新的方法来检测和分类街道和停车场的道路使用者。**通过道路使用者的分类，它意味着首先区分自行车与行人和机动车，然后将机动车区分为汽车、卡车、公交车、救护车等子类别。找到**这个问题的解决方案将使我们的工业合作伙伴Genetec能够增强他们的产品的能力。**例如，知道车辆的类别可以验证车牌或门禁设备**是否被盗或篡改。或者，它可以用来限制某些类型的道路使用者进入某个区域。为了**解决这个问题，我们的方法将依靠深度学习技术来学习对道路用户进行检测和分类。**我们将使用深度神经网络(DNN)的层次结构来从粗略的道路用户分类到**更精细的分类，以获得更具体的类别。从设计这种检测和**分类方法中获得的知识将转移到我们的工业合作伙伴Genetec。这将使Genetec在快速发展的视频监控市场保持**竞争力。能够对道路用户进行分类将使**Genetec能够开发更好的工具来监控交通并提升其产品的功能。此外，**PRS Bilodeau和Saunier都在致力于将计算机视觉应用于交通运输，特别是**交通数据收集和道路安全分析。道路使用者分类在**科学文献中没有被广泛讨论。这项工作也将有益于计算机视觉领域的更细颗粒目标检测**任务。最后，该项目将培养一名机器学习和数据科学领域的博士生，他们拥有加拿大行业非常需要的技能**。

The goal of this project is to design new methods to detect and classify road users on streets and parking lots.**By classification of road users, it is meant to distinguish first bikes from pedestrians and motorized vehicles,**and then to distinguish motorized vehicles into subcategories like cars, trucks, buses, ambulances, etc. Finding**a solution to this problem will allow our industrial partner Genetec to enhance the capabilities of their products.**For instance, knowing the category of a vehicle allows verifying if a license plate or an access control device**has been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. To**solve this problem, our approach will rely on deep learning techniques to learn to detect and classify road users.**We will use a hierarchy of Deep Neural Networks (DNNs) to go from a coarse categorization of road users to a**finer one to obtain more specific categories. The knowledge gained from the design of this detection and**classification method will be transferred to our industrial partner Genetec. This will allow Genetec to remain**competitive in the fast evolving video surveillance market. Being able to categorize road users will allow**Genetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, both**Prs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular for**traffic data collection and road safety analysis. Road user classification has not been addressed extensively in**the scientific literature. This work will also benefit the computer vision field for finer grain object detection**tasks. Finally, this project will train one PhD student in the machine learning and data science fields with skills**that are in high demand in the Canadian industry.