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

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。 将开发基于多视角机器学习的算法，该算法首次对成像传感器中的部分遮挡以及由于不同照明和天气条件引起的光散射具有鲁棒性。 税务局和里贾纳大学(UofR)之间的密切合作将在产业界和学术界之间建立独特的伙伴关系，为两所学院培训高素质人员铺平道路。 此外，运输车辆检测设施，如全球运输枢纽(GTH)的设施，将被用作离UOFR很近的活实验室，在那里我们的研究成果和发现可以被测试和利用。 该项目有可能导致在萨斯喀彻温省首次发展一门新兴的智能交通工程学科。

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.