Estimating human actions affinities across views

This paper deals with the problem of estimating the affinity level between different types of human actions observed from different viewpoints. We analyse simple repetitive upper body human actions with the goal of producing a view-invariant model from simple motion cues, that have been inspired by studies on the human perception. We adopt a simple descriptor that summarizes the evolution of spatio-temporal curvature of the trajectories, which we use for evaluating the similarity between actions pair on a multi-level matching. We experimentally verified the presence of semantic connections between actions across views, inferring a relations graph that shows such affinities.