Earticle

초록

영어

To realize the gesture recognition of high precision ratio, the gesture recognition method of multi-model data fusion based on Kinect depth image is proposed, to implement the automatic splicing of models. First of all, the feature package model uses the speeded up robust feature (SURF) algorithm to replace the scale invariant feature transform (SIFT) algorithm to extract features, improve the real-time performance. Secondly, Hu moment is introduced to describe the global gesture features, further improving the recognition rate, the ray casting is used finally, and the obtained coordinate information is used to solve the rigid transformation between two point cloud models. Finally, the proposed data fusion method is verified through two experiments, the algorithm in this paper is better than the traditional support vector machine (SVM) method both in real time performance and recognition rate, and obtains better model splicing effect.

목차

Abstract
 1. Introduction
 2. Gesture Recognition Method
  2.1. Feature Extraction
  2.2. Feature Merging
  2.3. Feature Matching and Transformation Matrix
  2.4. Fine Matching and Data Splicing
  2.5. Ray Casting
 3. Experiment Result and Analysis
  3.1. Experiment Preparation
  3.2. Experiment Result and Analysis
 4. Conclusion
 References

키워드

저자

• Jinghui Wang [ Beijing Forestry University, Beijing, China ]
• Mingzhi Niu [ Beijing Enfeisi technology co. ltd., Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605