Combining features for Chinese sign language recognition with Kinect
| Autor: | Xin Ma, Jason Gu, Hanbo Wu, Bingxia Xue, Lubo Geng, Yibin Li |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Engineering
business.industry Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Spherical coordinate system Sign language Chinese Sign Language language.human_language Support vector machine language Feature (machine learning) Computer vision Artificial intelligence business Gesture Extreme learning machine |
| Zdroj: | ICCA |
| DOI: | 10.1109/icca.2014.6871127 |
| Popis: | In this paper, we propose a novel three-dimensional combining features method for sign language recognition. Based on the Kinect depth data and the skeleton joints data, we acquire the 3D trajectories of right hand, right wrist and right elbow. To construct feature vector, the paper uses combining location and spherical coordinate feature representation. The proposed approach utilizes the feature representation in spherical coordinate system effectively depicting the kinematic connectivity among hand, wrist and elbow for recognition. Meanwhile, 3D trajectory data acquired from Kinect avoid the interference of the illumination change and cluttered background. In experiments with a dataset of 20 gestures from Chinese sign language, the Extreme Learning Machine(ELM) is tested, compared with Support Vector Machine( SVM), the superior recognition performance is verified. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|