  Human interaction recognition is a challenging problem in computer vision and
has been researched over the years due to its important applications. With the
development of deep models for the human pose estimation problem, this work
aims to verify the effectiveness of using the human pose in order to recognize
the human interaction in monocular videos. This paper developed a method based
on 5 steps: detect each person in the scene, track them, retrieve the human
pose, extract features based on the pose and finally recognize the interaction
using a classifier. The Two-Person interaction dataset was used for the
development of this methodology. Using a whole sequence evaluation approach it
achieved 87.56% of average accuracy of all interaction. Yun, et at achieved
91.10% using the same dataset, however their methodology used the depth sensor
to recognize the interaction. The methodology developed in this paper shows
that an RGB camera can be as effective as depth cameras to recognize the
interaction between two persons using the recent development of deep models to
estimate the human pose.
