基于运动基元的动作相似性判断
Action similarity judgment based on kinematic primitives
摘要：
了解人类依赖哪些特征--在视觉上识别动作相似性是从学习和发展的角度更清晰地了解人类动作感知的关键一步。在目前的工作中，我们调查在何种程度上的计算模型运动学的基础上可以确定动作的相似性和它的性能如何涉及到人类的相似性判断相同的行动。为此，12名参与者执行动作相似性任务，并将他们的表现与解决相同任务的计算模型的表现进行比较。所选择的模型有其根源，在发展机器人学和执行动作分类的基础上学习的运动基元。对比实验结果表明，该模型和人类参与者都能可靠地识别两个动作是否相同。然而，该模型产生更多的错误命中，并且比人类参与者具有更大的选择偏差。一个可能的原因是模型对所呈现动作的运动学基元的特别敏感。在第二个实验中，人类参与者在动作识别任务上的表现表明，他们只依赖于运动学信息，而不是动作语义。结果表明，在基于运动学特征的动作相似性任务中，该模型和人的表现都具有较高的准确性，可以为人类动作分类提供必要的依据。
英文摘要：
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.
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