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.