Title

View-Invariance In Action Recognition

Keywords

Action Recognition; Activities; Events; Spatiotemporal curvature; Video Understanding; View-invariant Representation

Abstract

Automatically understanding human actions using motion trajectories derived from video sequences is a very challenging problem. Since an action takes place in 3-D, and is projected on 2-D image, depending on the viewpoint of the camera, the projected 2-D trajectory may vary. Therefore, the same action may have very different trajectories, and trajectories of different actions may look the same. This may create a problem in interpretation of trajectories at the higher level. However, if the representation of actions only captures characteristics, which are view-invariant, then the higher level interpretation can proceed without any ambiguity. In most of the current work on action recognition, the issue of view invariance has been ignored. Therefore, proposed methods do not succeed in more general situations. In this paper, we first present a view-invariant representation of action consisting of dynamic instants and intervals, which is computed using spatiotemporal curvature of a trajectory. Then this representation is used by our system to learn human actions without any training. The system is able to incrementally learn different actions starting with no model. It can discover instances of the same action performed by different people, and in different viewpoints.

Publication Date

12-1-2001

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volume

2

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0035684337 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035684337

This document is currently not available here.

Share

COinS