Abstract
Tag is a popular children’s playground game. It revolves around taggers that chase and then tag runners, upon which their roles switch. There are many variations of the game that aim to keep children engaged by presenting them with challenges and different types of gameplay. We argue that the introduction of sensing and floor projection technology in the playground can aid in providing both variation and challenge. To this end, we need to understand players’ behavior in the playground and steer the interactions using projections accordingly. In this paper, we first analyze the behavior of taggers and runners in a traditional tag setting. We focus on behavioral cues that differ between the two roles. Based on these, we present a probabilistic role recognition model. We then move to an interactive setting and evaluate the model on tag sessions in an interactive tag playground. Our model achieves 77.96 % accuracy, which demonstrates the feasibility of our approach. We identify several avenues for improvement. Eventually, these should lead to a more thorough understanding of what happens in the playground, not only regarding player roles but also when the play breaks down, for example when players are bored or cheat.
Highlights
In children’s playgrounds, tag is one of the most popular games
Game settings can vary greatly, from professional sport scenarios, where automatic behavior analysis has been used to aid in understanding team strategies [8,26], to playground games such as tag, Marco Polo or hide-and-seek, where automatic recognition has been used to monitor children’s social skills and diagnose social conditions such as autism [28,35,38]
We consider tag games, but we present a probabilistic model that estimates the probability of a player being a tagger based on individual, pairwise and global cues
Summary
In children’s playgrounds, tag is one of the most popular games. Players assume one of two roles: tagger and runner. We identify potentially useful behavioral cues to distinguish taggers and runners Based on these discriminating cues, we introduce a probabilistic model to automatically classify a player’s role. The model considers interactions between players as well as individual cues and global information of the game We apply this model on recordings of young adults playing tag in a multimodal interactive tag playground (ITP). Human behavior analysis has proven to be a challenging and interesting problem in computer vision research Its applications, such as pedestrian tracking or activity recognition (see [1,33] for overviews) extend to diverse settings such as public spaces [2], political debates or conference rooms [10]. There has been a shift of focus from individuals towards the analysis of group behavior, for instance to analyze pedestrian movement [32] or determine group activity [6]
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