Exploring AI-based Video Segmentation and Saliency Computation to Optimize Imagery-acquisition from Moving Vehicles

The research team will first establish a test bed for the development of the advanced WIM (A-WIM) system by collaborating with local transportation agencies for the selection of the test bed site near a static weighing station. Then, it will develop a set of calibration procedures to guarantee that the level of accuracy is reached and preserved over time. These procedures will include, but are not limited to, the effect of temperature, humidity, and pavement type.

Integrated Analytics and Visualization for Multi-Modality Transportation Data

This research project aimed to develop a data-driven approach for modeling cities, with a focus on pedestrian dynamics, which play a fundamental role in urban planning. It focused on detecting and counting objects such as pedestrians, cars, and bicycles in visual data sources that can provide insight into how people move around a city. The research team used an image database made up of tens of millions of images produced by Brooklyn-based start-up Carmera as its main data source.