earticle

논문검색

Targets Association across Multiple Cameras by Learning Transfer Models

초록

영어

In this paper, we propose a novel method to solve the problem of targets association and tracking across multiple cameras of non-overlapping views. The method is divided into two parts. One is an improvement on appearance transfer model, another is an improvement on spatio-temporal transfer model. To learn inter-camera appearance transfer models, lαβ color space is used to calibrate images. By this way, the overall and local information can be used, which has advantage to color transform correction. To learn spatio-temporal transfer model, entry/exit zones of a non-overlapping topology can be effectively estimated by defining valid link and using clustering method. Then a kind of time constrain is set between two nodes to judge whether there is correlation of observations. Experiments show the effectiveness of the proposed method.

목차

Abstract
 1. Introduction
 2. Improved on Appearance Transfer Model
 3. Improved on Spatio-temporal Transfer Model
 4. Experimental Results
 5. Conclusion
 References

저자정보

  • Liu Suolan School of Automation, Southeast University, Nanjing 210096, China, School of Information Science & Engineering, Changzhou University, Changzhou 213164, China, Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
  • Wang Jia School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
  • Sun Changyin School of Automation, Southeast University, Nanjing 210096, China

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.