Earticle

초록

영어

In order to solve the problem of pedestrian detection performance, the described operator was improved. In this paper, semantic local binary pattern (SLBP) and histogram of oriented gradient (HOG) are combined as new feature operator. This feature method would enrich the information and enhance the detection performance. And then histogram intersection kernel support vector machine (HIKSVM) classifier is trained by the augment feature. Because the time cost is too large by the conventional SVM. HIKSVM could make up this drawback, and significantly reduce the training time. The experiments on the INRIA pedestrian dataset show that the method obtained significant improvement in accuracy comparing to HOG descriptors.

목차

Abstract
 1. Introduction
 2. The Overview of the Proposed Human Detection Algorithm
 3. Detailed Description of HOG Feature, SLBP Feature and HIKSVM Classifier
  3.1 HOG Feature
  3.2. SLBP Feature
  3.3 The Description of HIKSVM
 4. Experimental Results and Analysis
 5. Conclusio
 References

키워드

저자

• Aili Wang [ Higher Education Key Lab for Measuring & Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, China ]
• Mingxiao Wang [ Higher Education Key Lab for Measuring & Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, China ]
• Jitao Zhang [ Higher Education Key Lab for Measuring & Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, China ]
• Yuji Iwahori [ Dept. of Computer Science, Chubu University, Japan ]
• Bo Wang [ Higher Education Key Lab for Measuring & Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, 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