Deep Convolutional Neural Networks for Identification, Tracking, and Classification of Transport Vehicles using Multiple-view Imaging Systems

使用多视图成像系统的深度卷积神经网络对运输车辆进行识别、跟踪和分类

• 批准号: 536767-2018
• 负责人: Mehrandezh, Mehran
• 金额: $ 2.51万
• 依托单位: University of Regina
• 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=685427
• 关键词: Deep; Convolutional; Neural; Networks; Identification

This project revolves around the concept of Identification, Tracking, and Classification (ITC) of transport vehicles passing through inspection stations using imaging sensors. Multiple-view machine-learning-based algorithms will be developed that is robust to partial occlusion in the imaging sensors and to the light scatter due to differential illumination and weather conditions for the first time.A close collaboration between IRD and the University of Regina (UofR) will generate a unique partnership between industry and academia that can pave the way towards training highly-qualified personnel at both institutes. Furthermore, the transport vehicle inspection facilities, such as the one in the Global Transportation Hub (GTH) will be used as a living lab located at close proximity to the UofR, where our research results and findings can be tested and utilized. This project has the potential to lead to developing an emerging engineering discipline in intelligent transportation in Saskatchewan for the first time.

该项目围绕使用成像传感器通过检查站的运输车辆的识别，跟踪和分类（ITC）的概念。 将开发基于多个机器学习的算法，这对于第一次在成像传感器中的部分阻塞和光线散射非常可靠。IRD与里德大学（UOFR）之间的紧密合作将在行业和学术界之间产生一个独特的合作伙伴，可以在培训高度培训的人群中，以培养培训的人。 此外，运输车辆检查设施，例如全球运输中心（GTH）的运输车设施将被用作一个靠近UOFR的生活实验室，在此可以测试和利用我们的研究结果和发现。该项目有可能首次在萨斯喀彻温省建立智能运输的新兴工程学科。