Earticle

초록

영어

The spatio-temporal (ST) position information between local features plays an important role in action recognition task. To use the information, neighborhood-based features are built for describing local ST information around ST interest points. However, traditional methods of constructing neighborhood, such as sub-ST volumetric method and nearest-neighbor-based neighborhood method, ignore the orientation information of neighborhood. To make the neighborhood-based features more discriminative, we construct a novel, oriented neighborhood by imposing weights on the distance components. Specifically, in our scheme, firstly, local features are produced, and encoded by locality-constrained linear coding (LLC). Then, oriented neighborhoods are constructed by imposing weights on the distance components between features, and obtain single-scale oriented neighborhood features (SONFs). Next, multi-scale oriented neighborhood features (MONFs) are formed by concatenating SONFs. As a result, action video sequences are represented as a collection of MONFs. Finally, locality-constrained group sparse representation (LGSR) is used as classifier upon MONFs. Experimental results on the KTH and UCF Sports datasets show that our method achieves better performance than the competing local ST feature-based human action recognition methods.

목차

Abstract
 1. Introduction
 2. Detecting Spatio-temporal Interest Points (STIPs)
 3. Encoding Local Features by Locality-constrained Linear Coding (LLC)
  3.1. VQ, SVQ and SC
  3.2. Locality-constrained Linear Coding (LLC)
 4. Building Multi-scale Oriented Neighborhood Features (MONFs)
 5. Classifying with LGSR
 6. Experiments
  6.1. Experimental Setup
  6.2. Human Action Datasets
  6.3. Experimental Results and Analysis
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Jiangfeng Yang [ School of Communication and Information Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, No.2006, West Hi-Tech Zone, 61173 ]
• Zheng Ma [ School of Communication and Information Engineering School of Electronic Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, No.2006, West Hi-Tech Zone, 61173 ]
• Mei Xie [ School of Electronic Engineering University of Electronic Science and Technology of China, Xiyuan Ave, No.2006, West Hi-Tech Zone, 61173 ]

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