Abstract

In this paper, we present a novel approach for human in- teraction recognition from videos. We introduce high-level descriptions called interactive phrases to express binary semantic motion relation- ships between interacting people. Interactive phrases naturally exploit human knowledge to describe interactions and allow us to construct a more descriptive model for recognizing human interactions. We propose a novel hierarchical model to encode interactive phrases based on the latent SVM framework where interactive phrases are treated as latent variables. The interdependencies between interactive phrases are explic- itly captured in the model to deal with motion ambiguity and partial occlusion in interactions. We evaluate our method on a newly collected BIT-Interaction dataset and UT-Interaction dataset. Promising results demonstrate the effectiveness of the proposed method.