Title: An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions,
Abstract: In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is inspired by the strong evidence in the applied perception
community that humans perform recognition in a foveated manner, that is they
recognize events or objects by only focusing on visually significant aspects.
An important contribution of our method is that the computation of the weights
assigned to body parts is invariant to viewing directions and camera parameters
in the input data. We have performed extensive experiments to validate the
proposed approach and demonstrate its significance. In particular, results show
that considerable improvement in performance is gained by taking into account
the relative importance of different body parts as defined by our approach.