earticle

논문검색

Multiple-shot People Re-identify based on Feature Selection with Sparsity

원문정보

Dongping Zhang, Yanjie Li, Jiao Xu, Ye Shen

보안공학연구지원센터(IJHIT) International Journal of Hybrid Information Technology Vol.8 No.1 2015.01 pp.27-34
피인용수 : 0건 (자료제공 : 네이버학술정보)

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

저자정보

  • 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

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.