An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions
This addresses the challenge of action recognition in video for computer vision applications, but it is incremental as it builds on existing methods by adding a weighting scheme.
The paper tackles the problem of recognizing human actions in video by showing that different body parts have varying importance, and proposes a method to assign invariant weights to body parts, resulting in considerable performance improvement.
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.