Deep Learning

We propose a novel approach to multi-object tracking that leverages Normalizing Flows to learn a joint probability distribution over the costs of candidate associations. Our experiments show that our approach consistently enhances the performance of several tracking-by-detection algorithms.

This work tackles Weakly Supervised Anomaly detection within the field of self-supervised learning, asking the model to yield the same anomaly scores for different augmentations of the same video sequence.

Autmatically keep the internal air quality at its best, with pollution prediction.

Vision and Cognitive Systems Project 2019/2020.