Using deep learning to detect and track all modes in traffic videos

交通网络规划、交通安全和智能交通系统需要起点-终点（OD）数据来协助设计和规划道路、十字路口和现代环形交叉路口等基础设施。OD数据传统上是通过手动注释所有城市旅行模式（例如，车辆，过境，行人和骑自行车的人）的视频来收集的。手动注释非常繁琐，因为需要在视频片段中为所有模式的进入和退出添加时间戳。它需要连续几个小时播放、暂停、倒带一个长视频。一种中间解决方案已经出现，供应商将他们的交通视频发送到软件服务（如MioVision），该软件服务首先通过自动和手动方法的组合处理视频，然后将OD数据发送回供应商。在这个研究项目中，ISL工程服务公司与阿尔伯塔大学计算科学系的Nilanjan Ray博士合作，利用深度学习和强化学习来创建计算机视觉算法，从而完全自动化从交通视频中生成OD数据。这项研究将是建立一个用于OD数据收集和交通研究的完全自动化视频分析工具原型的第一步，这在加拿大市场上是不存在的。

Transportation network planning, traffic safety and intelligent transportation systems require origin-destination (OD) data to assist in designing and planning for infrastructure such as roadways, intersections and modern roundabouts. OD data is traditionally collected by manually annotating videos of all modes of urban travel (e.g., vehicles, transit, pedestrians and cyclists). Manual annotation is extremely tedious, because one needs to timestamp the entrance and the exit of all modes in a video segment. It requires starting, pausing, rewinding a long video for hours. An intermediate solution has emerged, where vendors send their traffic videos to a software service (such as MioVision) that first processes the videos by a combination of automatic and manual methods, then sends back OD data to the vendors. In this research project, ISL Engineering Services is partnering with Dr. Nilanjan Ray in the Dept. of Computing Science, University of Alberta to utilize deep learning and reinforcement learning to create computer vision algorithms that will completely automate the OD data generation from traffic videos. This research would be a first step toward building a prototype of a completely automated video analysis tool for OD data collection and traffic studies that does not exist in the Canadian market.