Behavior Recognition in Human Object Interactions with a Task Model
Sydney, Australia（2006）
摘要
Behavior recognition can be greatly levitated by a model of the task with which the behavior is associated. We present a Markovian task model that captures the temporal relations between subtasks, and provides prior information that helps behavior recognition from simple and noisy sensory data. A Dynamic Bayesian Network is used to integrate multi-model evidence and infer underlying task states. Experiments demonstrate that this system can recognize human behavior in everyday tasks such as making sandwiches.
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关键词
human object interactions,markovian task model,dynamic bayesian network,behavior recognition,multi-model evidence,noisy sensory data,human behavior,everyday task,task model,prior information,infer underlying task state,temporal relation,history,data security,bayesian methods,human computer interaction,tracking,computer security,computer science
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