運動学的プリミティブに基づく動作類似性判断
Action similarity judgment based on kinematic primitives
学習した運動学的プリミティブで動作の類似性を判定する計算モデルを構築し、人間の判断と比較した。両者とも高精度だが、モデルは誤検出と選択バイアスが多いことを示した。
著者: Vipul Nair, Paul Hemeren, Alessia Vignolo, Nicoletta Noceti, Elena Nicora, Alessandra Sciutti, Francesco Rea, Erik Billing, Francesca Odone, Giulio Sandini
分類: cs.RO, cs.LG
原文アブストラクト
Understanding which features humans rely on -- in visually recognizing action similarity is a crucial step towards a clearer picture of human action perception from a learning and developmental perspective. In the present work, we investigate to which extent a computational model based on kinematics can determine action similarity and how its performance relates to human similarity judgments of the same actions. To this aim, twelve participants perform an action similarity task, and their performances are compared to that of a computational model solving the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experiment results show that both the model and human participants can reliably identify whether two actions are the same or not. However, the model produces more false hits and has a greater selection bias than human participants. A possible reason for this is the particular sensitivity of the model towards kinematic primitives of the presented actions. In a second experiment, human participants' performance on an action identification task indicated that they relied solely on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions.
