Abstract:
Estimating the most dominant person in a social interaction setting
is a challenging feat even with the advancement of deep learning
techniques due to problem complexity, non-availability of labelled
data and subjective biases in annotations. This paper aims to refor-
mulate the problem of detecting the Most Dominant Person (MDP)
as a person ranking problem by utilizing person-specifc attributes
such as facial gestures, eye gaze, visual attention and speaking pat-
terns. Our proposed framework, attributed Graph-based dominant
person ranking in social InTeracTIon videos, GraphITTI, learns
generic and robust person rankings on top of context level features.
To inject domain knowledge into the GraphITTI framework, we
consider inter-personal and intra-personal aspects along with spa-
tiotemporal context patterns. Our extensive quantitative analysis
suggests that GraphITTI framework performs favourably over the
current state-of-the-art for dominant person detection and ranking.
The code is available at https://github.com/shgnag/GraphITTI.
