Detection and classification of road users for video surveillance applications

视频监控应用中的道路使用者检测和分类

• 批准号: 508883-2017
• 负责人: Bilodeau, GuillaumeAlexandre
• 金额: $ 1.28万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676828
• 关键词: Detection; classification; road; users; video

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. Findinga 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 devicehas been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. Tosolve 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 afiner one to obtain more specific categories. The knowledge gained from the design of this detection andclassification method will be transferred to our industrial partner Genetec. This will allow Genetec to remaincompetitive in the fast evolving video surveillance market. Being able to categorize road users will allowGenetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, bothPrs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular fortraffic data collection and road safety analysis. Road user classification has not been addressed extensively inthe scientific literature. This work will also benefit the computer vision field for finer grain object detectiontasks. Finally, this project will train one PhD student in the machine learning and data science fields with skillsthat are in high demand in the Canadian industry.

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。通过对道路使用者的分类，首先要将自行车与行人、机动车区分开来，然后将机动车划分为汽车、卡车、公共汽车、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能够增强其产品的功能。例如，了解车辆的类别可以验证车牌或访问控制设备是否被盗或被篡改，或者可以用来限制特定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。道路使用者分类在科学文献中还没有得到广泛的讨论。这项工作也将有利于计算机视觉领域的细颗粒目标检测任务。最后，该项目将培养一名加拿大工业界急需的机器学习和数据科学领域的博士研究生。