Earticle

초록

영어

Action tracking and recognition is a challenge due to human deformation and complex scene system. Tracking-by-detection methods are used to solve appearance changes problem caused by viewpoint, occlusion, scale or deformation. Here we propose a robust object tracking and generative action recognition method. Compressive sensing is improved to track object with superpixels, and the generative structural part model is designed to be adaptive to variation of deformable object. We evaluate the method on challenging sequences. Also, we make qualitative and quantitative discussion. The results indicate the method is robust, and it is adaptive to deformable object tracking and action recognition.

목차

Abstract
 1. Introduction
 2. Compressive Representation
 3. Generative Structural Method
  3.1 Structural Model
  3.2 Generative Parts Method
  3.3 Algorithm Details
 4. Results and Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Gaofeng Li [ School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China ]
• Fei Wang [ School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China ]
• Wang Lei [ School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Chinesisch-Deutsches Hochschulkolleg, Tongji University, Shanghai 201804, 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