Pose-based human action recognition via sparse representation in dissimilarity space
A framework for pose-based human action recognition is proposed, where coordinates of joints are considered as inputs. By comparing corresponding trajectories, actions are represented by vectors of dissimilarities to a set of prototypes. The task of recognition is performed into the dissimilarity space. The newly created UPCV Action dataset is introduced, consisting of skeletal data for 10 actions. Evaluation on three datasets confirms better performance compared to other pose-based and depth-based methods.
action recognition sparse representation dissimilarity representation pose representation articulated human motion rgb-d sensors angular features pose encoding