Locating Human Interactions With Discriminatively Trained Deformable Pose+Motion Parts

Abstract

We model dyadic (two-person) interactions by discriminatively training a spatio-temporal deformable part model of fine-grained human interactions. All interactions involve at most two persons. Our models are capable of localizing human interactions in unsegmented videos, marking the interactions of interest in space and time. Our contributions are as follows: First, we create a model that localizes human interactions in space and time. Second, our models use multiple pose and motion features per part. Third, we experiment with different ways of training our models discriminatively. When testing on the target class our models achieve a mean average precision score of 0.86. Cross dataset tests show that our models generalize well to different environments.