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

该项目围绕着使用成像传感器对通过检查站的运输车辆进行识别、跟踪和分类（ITC）的概念展开。将首次开发基于多视图机器学习的算法，该算法对成像传感器中的部分遮挡和由于不同光照和天气条件造成的光散射具有鲁棒性。税务局与里贾纳大学（UofR）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，为两所学院培养高素质人才铺平道路。此外，全球交通枢纽（GTH）等运输车辆检验设施将被用作靠近UofR的生活实验室，可以对我们的研究成果和发现进行测试和利用。该项目有可能首次在萨斯喀彻温省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.