We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs.
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data.
Online grooming (OG) of children is a pervasive issue in an increasingly interconnected world.
Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance.
We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data.
Our method is extensively evaluated on a augmented version of the QM9 dataset that includes unstable molecules, as well as a new dataset of infinite- and finite-size crystals, and is compared with the Message Passing Neural Network (MPNN).
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.