View-invariant action recognition
release_bxzcalkkxrhdrlrm4cj4xy7oci
by
Yogesh S Rawat, Shruti Vyas
2020
Abstract
Human action recognition is an important problem in computer vision. It has a
wide range of applications in surveillance, human-computer interaction,
augmented reality, video indexing, and retrieval. The varying pattern of
spatio-temporal appearance generated by human action is key for identifying the
performed action. We have seen a lot of research exploring this dynamics of
spatio-temporal appearance for learning a visual representation of human
actions. However, most of the research in action recognition is focused on some
common viewpoints, and these approaches do not perform well when there is a
change in viewpoint. Human actions are performed in a 3-dimensional environment
and are projected to a 2-dimensional space when captured as a video from a
given viewpoint. Therefore, an action will have a different spatio-temporal
appearance from different viewpoints. The research in view-invariant action
recognition addresses this problem and focuses on recognizing human actions
from unseen viewpoints.
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