UrbanSensing

Started in 2014 we deployed a large scale 3D visual sensing infrastructure in Lisbon’s Airport. Currently with more than 30 depth cameras (3D sensors), became a key technology for queue…

Smartbike

Monitoring cyclists’ data is a keystone to foster urban cyclists’ safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the route risk from the cyclist perspective.

Vehicle Counting

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).