Augmented skeletal joints for temporal segmentation of sign language actions

Author

Seddik, Bassem ; Gazzah, Sami ; Chateau, Thierry ; Ben Amara, Najoua Essoukri

Author_Institution

Univ. of Sousse, Sousse, Tunisia

fYear

2014

Firstpage

1

Lastpage

6

Abstract

We present in this paper a novel solution for temporal segmentation of human gestures that takes advantage of the skeletal-joints streams offered by the Kinect sensor. Our contribution consist in introducing an improved skeletal representation and its usage in a multilayer motion delimitation that distinguishes the non-vocabulary actions. The evaluation of the solution is presented on a subset of the Chalearn Gesture Challenge (CGC) 2014 dataset. The obtained temporal segmentation is better than the CGC baseline methods and has proved to be important for the task of human-action recognition.

Keywords

image representation; image segmentation; image sensors; sign language recognition; CGC 2014 dataset; CGC baseline methods; Chalearn Gesture Challenge 2014 dataset; augmented skeletal joints; human-action recognition; improved skeletal representation; multilayer motion delimitation; nonvocabulary actions; skeletal-joint Kinect sensor; temporal human gesture segmentation; temporal sign language action segmentation; Feature extraction; Gesture recognition; Image segmentation; Joints; Motion segmentation; Support vector machines; Vocabulary; Chalearn Gesture Competition; Humanaction; Kinect; Skeletal-joints; Temporal segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, Applications and Systems Conference (IPAS), 2014 First International

Print_ISBN

978-1-4799-7068-1

Type

conf

DOI

10.1109/IPAS.2014.7043295

Filename

7043295