Human interaction recognition based on the co-occurence of visual words

This paper describes a novel methodology for automated recognition of high-level activities. A key aspect of our framework relies on the concept of cooccurring visual words for describing interactions between several persons. Motivated by the numerous success of human activity recognition methods using bag-of-words, this paradigm is extended. A 3-D XYT spatio-temporal volume is generated for each interacting person and a set of visual words is extracted to represent his activity. The interaction is then represented by the frequency of co-occurring visual words between persons. For our experiments, we used the UT-interaction dataset which contains several complex human-human interactions.