One of the most common problems preventing the application of prediction models in the
real world is lack of generalization: The accuracy of models, measured in the benchmark …

Driver observation models are rarely deployed under perfect conditions. In practice,
illumination, camera placement and type differ from the ones present during training and …

We explore the problem of automatically inferring the amount of kilocalories used by human
during physical activity from his/her video observation. To study this under researched task …

While deep Convolutional Neural Networks (CNNs) have become front-runners in the field
of driver observation, they are often perceived as black boxes due to their end-to-end nature …

Deep learning-based models are at the top of most driver observation benchmarks due to
their remarkable accuracies but come with a high computational cost, while the resources …

Driver activity classification is crucial for ensuring road safety, with applications ranging from
driver assistance systems to autonomous vehicle control transitions. In this paper, we …

This work consists a master's thesis conducted in Computer Engineering: AI, Vision &
Sound. It describes the work conducted during a semester abroad at UC San Diego. It is …

This thesis addresses the challenge of estimating caloric expenditure from videos capturing
individuals engaging in a variety of activities, ranging from mild to intense exercises. The …