Triangle charades: a data-collection game for recognizing actions in motion trajectories

Humans have a remarkable tendency to anthropomorphize moving objects, ascribing to them intentions and emotions as if they were human. Early social psychology research demonstrated that animated film clips depicting the movements of simple geometric shapes could elicit rich interpretations of intentional behavior from viewers. In attempting to model this reasoning process in software, we first address the problem of automatically recognizing humanlike actions in the trajectories of moving shapes. There are two main difficulties. First, there is no defined vocabulary of actions that are recognizable to people from motion trajectories. Second, in order for an automated system to learn actions from motion trajectories using machine-learning techniques, a vast amount of these action- trajectory pairs is needed as training data. This paper describes an approach to data collection that resolves both of these problems. In a web-based game, called Triangle Charades, players create motion trajectories for actions by animating a triangle to depict those actions. Other players view these animations and guess the action they depict. An action is considered recognizable if players can correctly guess it from animations. To move towards defining a controlled vocabulary and collecting a large dataset, we conducted a pilot study in which 87 users played Triangle Charades. Based on this data, we computed a simple metric for action recognizability. Scores on this metric formed a gradual linear pattern, suggesting there is no clear cutoff for determining if an action is recognizable from motion data. These initial results demonstrate the advantages of using a game to collect data for this action recognition task.