Hand Orientation Estimation in Probability Density Form
This work addresses hand orientation estimation for understanding human activities, but it appears incremental as it builds on existing methods by introducing a probability density representation.
The authors tackled the problem of hand orientation estimation by proposing a new method that represents orientation in probability density form, which addresses the cyclicity issue and allows integration of multiple predictions; they validated the method on a dataset of cooperative group work, though no concrete performance numbers were provided.
Hand orientation is an essential feature required to understand hand behaviors and subsequently support human activities. In this paper, we present a new method for estimating hand orientation in probability density form. It can solve the cyclicity problem in direct angular representation and enables the integration of multiple predictions based on different features. We validated the performance of the proposed method and an integration example using our dataset, which captured cooperative group work.