Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph

TL;DR

This paper introduces a gesture recognition method emphasizing key actions by employing a path-searching algorithm in a weighted graph, improving the differentiation of similar gestures such as sign language words.

Contribution

It presents a novel approach combining optical flow, eigenspace representation, and path-searching in weighted graphs to focus on important actions in gesture recognition.

Findings

Effective differentiation of similar sign language gestures.

Improved recognition accuracy for important actions.

Validation through experimental results.

Abstract

This paper proposes a method of gesture recognition with a focus on important actions for distinguishing similar gestures. The method generates a partial action sequence by using optical flow images, expresses the sequence in the eigenspace, and checks the feature vector sequence by applying an optimum path-searching method of weighted graph to focus the important actions. Also presented are the results of an experiment on the recognition of similar sign language words.