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: $ 1.23万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696446
• 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. 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.

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，它是为了区分第一辆自行车与行人和机动车， 然后将机动车辆分为轿车、卡车、公交车、救护车等子类别。 这一问题的解决方案将使我们的工业合作伙伴Genetec能够增强其产品的能力。 例如，知道车辆的类别可以验证车牌或门禁设备 被盗或篡改，或可被用来限制某些类型的道路使用者进入某一区域。至 要解决这个问题，我们的方法将依靠深度学习技术来学习对道路使用者进行检测和分类。 我们将使用深度神经网络(DNN)的层次结构来从对道路使用者的粗略分类到 更精细的一个，以获得更具体的类别。从这个探测器的设计中获得的知识 分类方法将转移到我们的工业合作伙伴Genetec。这将允许Genetec保留 在快速发展的视频监控市场中具有竞争力。能够对道路使用者进行分类将允许 GeNeTec将开发更好的工具来监控流量并提升其产品的性能。同样，两者都是 PRS Bilodeau和Saunier正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。年，道路使用者分类问题尚未得到广泛解决 科学文献。这项工作也将有益于计算机视觉领域的细粒度目标检测 任务。最后，该项目将在机器学习和数据科学领域培养一名具有技能的博士生 在加拿大的工业中需求量很大。