Computer Vision

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.

Vision and Cognitive Systems Project 2019/2020.