Deep Understanding of Urban Traffic from Large-Scale City Cameras
The challenge is to count vehicles in a city-scale low resolution, low frame rate network of urban cameras. The target city is NY where 200+ cameras stream video from selected places. In this work several Deep Learning solutions are presented with unprecedented performance. In a very diverse conditions (sunny, cloudy, rainy) the deep-learning model is able to estimate the correct number of cars with errors of 1.5 cars (MAE).