Labeling subtle conversational interactions within the CONVERSE dataset

The field of Human Action Recognition has ex- panded greatly in previous years, exploring actions and inter- actions between individuals via the use of appearance and depth based pose information. There are numerous datasets that display action classes composed of behaviors that are well defined by their key poses, such as ‘kicking’ and ‘punching’. The CONVERSE dataset presents conversational interaction classes that show little explicit relation to the poses and gestures they exhibit. Such a complex and subtle set of interactions is a novel challenge to the Human Action Recognition community, and one that will push the cutting edge of the field in both machine learning and the understanding of human actions. CONVERSE contains recordings of two person interactions from 7 conversational scenarios, represented as sequences of human skeletal poses captured by the Kinect depth sensor. In this study we discuss a method providing ground truth labelling for the set, and the complexity that comes with defining such annotation. The CONVERSE dataset it made available online