Data Analytics Methods

The CLASS software architecture also includes a set of advanced data-analytics methods. Interestingly, all these data analytics engines are optimized to execute at both, the edge and the cloud side, providing the required flexibility needed to distribute the computation of complex data analytics workflows composed of different analytics frameworks across the compute continuum.

The software component that manages data analytics is a modified version of a state-of-the-art Deep Neural Network (DNN) called YOLOv3. The DNN has been retrained using different datasets with respect to the original implementation to obtain a higher precision of the output. YOLO is implemented with tkDNN.

Link: https://github.com/class-euproject/tkDNN

A Tracker, based on Extended Kalman Filter, is a fast method to track objects from a camera. After the DNN detects the bounding-boxes of the objects, the central bottom point of the bounding box is taken as a reference of that object, and on that the tracker is instantiated.

Link: https://github.com/class-euproject/tracker_CLASS

A parallel Complex Event Processor (pCEP) is a parallelized version of Atos’ Complex Event Processing (CEP), for state and event inference, i.e., identifying relevant data among the multitude of information arriving to the system (e.g., movement of objects such as vehicles or persons, and traffic information). The pCEP engine, based in the Dolce language, receives primitive events (e.g., obstacle detected) and, depending on the Dolce rules, produces complex events (e.g., probability of collision with the obstacle detected).

Link: https://github.com/class-euproject/pCEP

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