Human Interactions

Summary

Recognizing human interactions from video is an important step forward towards the long-term goal of performing scene understanding fully automatically. Recent years have seen a concentration of works revolving around the problem of recognizing single person actions, as well as group activities, while the area of modeling the interactions between two people is still relatively unexplored. In (Motiian et al., 2013) people interactions are modeled by forming temporal interaction trajectories coupling together the body motion of each individual as well as their proximity relationships. Such trajectories live in a well-defined Riemannian manifold and enjoy specific symmetry properties that have to be taken into account during the development of a theoretically grounded recognition framework.

References

1. ISVC

   Pairwise Kernels for Human Interaction Recognition Motiian, S., Feng, K., Bharthavarapu, H., Sharlemin, S., and Doretto, G. In Advances in Visual Computing, 2013. Oral abstract bibTeX pdf html doi

   In this paper we model binary people interactions by forming tempo- ral interaction trajectories, under the form of a time series, coupling together the body motion of each individual as well as their proximity relationships. Such tra- jectories are modeled with a non-linear dynamical system (NLDS). We develop a framework that entails the use of so-called pairwise kernels, able to compare interaction trajectories in the space of NLDS. To do so we address the problem of modeling the Riemannian structure of the trajectory space, and we also prove that kernels have to satisfy certain symmetry properties, which are peculiar of this interaction modeling framework. Experiment results show that this approach is quite promising, as it is able to match and improve state-of-the-art classification and retrieval accuracies on two human interaction datasets.

   @incollection{motiianFBSD13isvc,
     abbr = {ISVC},
     author = {Motiian, S. and Feng, K. and Bharthavarapu, H. and Sharlemin, S. and Doretto, G.},
     title = {Pairwise Kernels for Human Interaction Recognition},
     booktitle = {Advances in Visual Computing},
     publisher = {Springer Berlin Heidelberg},
     year = {2013},
     volume = {8034},
     series = {Lecture Notes in Computer Science},
     pages = {210-221},
     bib2html_pubtype = {Conferences},
     bib2html_rescat = {Interaction Recognition, Video Analysis},
     doi = {10.1007/978-3-642-41939-3_21},
     file = {motiianFBSD13isvc.pdf:doretto/conference/motiianFBSD13isvc.pdf:PDF},
     isbn = {978-3-642-41938-6},
     owner = {doretto},
     timestamp = {2013.10.18},
     url = {http://dx.doi.org/10.1007/978-3-642-41939-3_21},
     wwwnote = {Oral}
   }