Earticle

초록

영어

Visual analysis has potential to be used for recognition, and it is one of the hottest but most difficult subjects in computer vision. In order to identify pedestrian movement in an Intelligent Security Monitoring System, the video activity in the prospect is represented by a series of spatio-temporal interest points. Since human posture has the characteristics of uncertainty and illegibility, the clustering centers of each class are computed by fuzzy clustering techniques. We presented a pedestrian classification method based on improved support vector machines in order to solve non-rigid objects that are difficult to identify in intelligent monitoring systems. The Support Vector Machine technology and the decision tree have combined into one multi-class classifier so as to solve multi-class classification problems. Then a full-SVM (Support Vector Machine) decision tree is constructed based on the conventional decision tree. At last, the method is evaluated on the KTH action dataset and receives a comparatively high correct recognition rate.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Pedestrian Classification
  2.2. Interest Point Methods
  2.3. Topic Models for Visual Recognition
  2.4. Codebook Formation
 3. Principle Theories
  3.1. Video Representation
  3.2. SVM
 4. Non-linear SVM Decision Tree
  4.1. Non-linear Decision Tree
  4.2. NSVM Decision Tree (NSVMDT)
 5. Experimental Results and Discussion
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Hongmin Xue [ School of Computer Science and Technology, Xidian University, Xi'an, P. R. China, Department of Computer Science and Technology, ShaanXi XueQian Normal University, Xi'an, P. R. China ]
• Zhijing Liu [ School of Computer Science and Technology, Xidian University, Xi'an, P. R. China ]
• Jing Xiong [ School of Computer Science and Technology, Xidian University, Xi'an, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605