Inferring stochastic regular grammar with nearness information for human action recognition

Authors:
Kyungeun Cho;Hyungje Cho;Kyhyun Um
Affiliations:
Department of Computer and Multimedia Engineering, Dongguk University, Seoul, Korea;Department of Computer and Multimedia Engineering, Dongguk University, Seoul, Korea;Department of Computer and Multimedia Engineering, Dongguk University, Seoul, Korea
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Year:
2006

Citing 2
Cited 1

A survey of computer vision-based human motion capture

Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
A Model Driven 3D Image Interpretation System Applied to Person Detection in Video Images

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1

Understanding video events: a survey of methods for automatic interpretation of semantic occurrences in video

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an extended scheme of human action recognition with nearness information between hands and other body parts for the purpose of automatically analyzing nonverbal actions of human beings. First, based on the principle that a human action can be defined as a combination of multiple articulation movements, we apply the inference of stochastic grammars. We measure and quantize each human action in 3D coordinates and make two sets of 4-chain-code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. Next, we extend the stochastic grammar inferring method by applying nearness information. We confirm that various physical actions are correctly classified against a set of real-world 3D temporal data with this method in experiments. Our experiments show that this extended method reveals comparatively successful achievement with a 92.7% recognition rate of 60 movements of the upper body.