We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data.
This paper presents a framework which uses computer vision algorithms to standardise images and analyse them for identifying crop diseases automatically.
We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes.
We present a new framework for vision-based estimation of calorific expenditure from RGB-D data - the first that is validated on physical gas exchange measurements and applied to daily living scenarios.
Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.