Computer Vision has played a major role in Intelligent Transportation Systems
(ITS) and traffic surveillance. Along with the rapidly growing automated
vehicles and crowded cities, the automated and advanced traffic management
systems (ATMS) using video surveillance infrastructures have been evolved by
the implementation of Deep Neural Networks. In this research, we provide a
practical platform for real-time traffic monitoring, including 3D
vehicle/pedestrian detection, speed detection, trajectory estimation,
congestion detection, as well as monitoring the interaction of vehicles and
pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5
deep neural network model for vehicle/pedestrian detection and an enhanced SORT
tracking algorithm. For the first time, a hybrid satellite-ground based inverse
perspective mapping (SG-IPM) method for camera auto-calibration is also
developed which leads to an accurate 3D object detection and visualisation. We
also develop a hierarchical traffic modelling solution based on short- and
long-term temporal video data stream to understand the traffic flow,
bottlenecks, and risky spots for vulnerable road users. Several experiments on
real-world scenarios and comparisons with state-of-the-art are conducted using
various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM
collected from highways, intersections, and urban areas under different
lighting and weather conditions.

本研究提供了一个基于深度学习的实时交通监控平台，通过单个交通摄像头实现了车辆 / 行人检测、速度检测、轨迹估计、拥堵检测、以及车辆和行人交互的监测，同时使用短期和长期的时间视频数据流开发了分层交通模型解决方案，实现了对实际交通情况的监测。