A discriminative key pose sequence model for recognizing human interactions

Abstract

In this paper we develop a model for recognizing human interactions - activity recognition with multiple actors. An activity is modeled with a sequence of key poses, important atomic-level actions performed by the actors. Spatial arrangements between the actors are included in the model, as is a strict temporal ordering of the key poses. An exemplar representation is used to model the variability in the instantiation of key poses. Quantitative results that form a new state-of-the-art on the benchmark UT-Interaction dataset are presented, along with results on a subset of the TRECVID dataset.