Detection and classification of road users for video surveillance applications

该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。**对道路使用者进行分类，首先是将自行车与行人、机动车辆区分开来，然后将机动车辆划分为汽车、卡车、公共汽车、救护车等子类。找到这个问题的解决方案将使我们的工业合作伙伴Genetec能够增强其产品的功能。例如，了解车辆的类别可以验证车牌或门禁设备是否被盗或被篡改，或者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检测和分类道路使用者。我们将使用深度神经网络（dnn）的层次结构，从道路使用者的粗略分类到更精细的分类，以获得更具体的类别。从这种检测和分类方法的设计中获得的知识将转移给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上保持竞争力。能够对道路使用者进行分类将使**Genetec能够开发更好的工具来监控交通并提高其产品的功能。此外，Bilodeau博士和Saunier博士都在研究计算机视觉在交通运输中的应用，特别是在交通数据收集和道路安全分析方面。在科学文献中，道路使用者分类尚未得到广泛讨论。这项工作也将有利于计算机视觉领域的细粒度目标检测任务。最后，该项目将培养一名机器学习和数据科学领域的博士研究生，其技能**在加拿大工业界是高需求的。

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.