Earticle

초록

영어

We focus on the issue of learning the topology of the non-overlapping multi-camera network, which includes recovering the nodes (entry and exit zones), transition time distribution and links. Firstly, the nodes associated with each camera view are identified using clustering method. Then, transition time distribution is modeled as a Gaussian distribution and is computed by accumulated cross correlation and Gaussian fitting. Finally, the mutual information is used to refine the possible links and the topology is recovered. Experimental results on simulated data and real scene demonstrate the effectiveness of the proposed method.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Topology Learning
  3.1. Nodes Learning
  3.2. Transition Time Distribution Learning
  3.3. Links Refining
 4. Experimental Results
 4.1. Experimental Results of Simulated Network
 4.2. Experimental Results of Real Network

키워드

저자

• Xiaolin Li [ College of Physics and Electronic engineering, Dezhou University, Dezhou 253023, China ]
• Wenhui Dong [ College of Physics and Electronic engineering, Dezhou University, Dezhou 253023, China, School of Control Science and Engineering, Shandong University, Jinan 250061, China ]
• Faliang Chang [ School of Control Science and Engineering, Shandong University, Jinan 250061, China ]
• Peishu Qu [ College of Physics and Electronic engineering, Dezhou University, Dezhou 253023, China ]

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