Earticle

초록

영어

In a video surveillance network, it is always required to track and recognize people when they move through the environment. This paper presents a novel re-identification method for multiple-people using feature selection with sparsity. By using the multiple-shot approach, each of appearance models is created in this method. The human body is divided into five parts form which the features of color, height, gradient were extracted respectively. Our appearance model is represented by linear regression method. Experimental results show that our appearance model is robust and attain a high precision rate and processing performance.

목차

Abstract
 1. Introduction
 2. Related Works
 3. The Proposed Method
  3.1. Pedestrian Detection
  3.2. Foreground Extraction and Body Part
  3.3. Part Appearance Feature Extraction
  3.4. Multiple Person Re-identification by Matching
 4. Experiments and Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Dongping Zhang [ College of Information Engineering, China Jiliang University, Hangzhou 310018, China ]
• Yanjie Li [ College of Information Engineering, China Jiliang University, Hangzhou 310018, China ]
• Jiao Xu [ College of Information Engineering, China Jiliang University, Hangzhou 310018, China ]
• Ye Shen [ College of Information Engineering, China Jiliang University, Hangzhou 310018, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605