Data Analytics Methods

The CLASS software architecture has been used to develop, deploy and execute three smart mobility use-cases, which include the following data-analytics methods:

–    Object detection, based on a convolutional deep neural network to determine the type of objects appearing in a video stream.
–    Object tracking, based on Kalman filters to compute the dynamics of object detected, including the trajectory, orientation, speed and acceleration.
–    Object deduplication, to handle objects simultaneously detected by multiple sources.
–    Trajectory prediction of detected objects aiming to provide an estimation of the most likely future positions of vehicles or pedestrians based on the history of their tracked positions.
–    Data aggregation, which is a fundamental function, responsible for the generation and maintenance of the Data Knowledge Base (DKB).
–    Collision detection, which detects potential collisions between road users based on their predicted trajectory, and generates alerts in real-time.
–    Pollution emission estimation, to provide a calculation of the pollutant particles emitted by the detected vehicles.

Find more details on the CLASS applications on our Use Case page.