Counting passengers and vehicles with computer vision techniques for city of Edmonton traffic

点击翻译按钮获取中文摘要

Passenger count for Light Rail Transit (LRT) and vehicle classification at the road intersections are important transit data that the City of Edmonton uses for transit and transportation planning. At present, the data collection is manual and it occurs only once in a year, because the cost of manual data collection is high. In this proposal we present a computer vision-based low-cost solution to these data collections that will occur 24/7 in a non-intrisive and automated manner. Our solution will use existing video cameras mounted at LRT stations and road intersection bringing the overall cost down only to the algorithm development and its deployment with personal computers equipped with graphics processing units (GPUs). On the computer vision side, our proposed solution will use promising state-of-the-art algorithms and their further development to the passenger counting and vehicle classification problems.