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.
Vahdat et al. REF developed a temporal model consisting of key poses for recognizing higher level activities.
A discriminative key pose sequence model for recognizing human interactions
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Computer Science