Earticle

초록

영어

3D-2D vehicle registration provides a new way for vehicle recognition, localization and tracking in traffic surveillance systems. In this paper we present two novel fitness functions to measure 3D-2D vehicle matching, where 3D wire-frame model is used. Unlike the existing vehicle registration methods, we group the model's wireframes into the important and unimportant ones in view of the disaccord between real vehicle and the wire-frame model. The important wireframes generally well fit the corresponding image edges, whereas the image edges corresponding to the unimportant wireframes may not exist due to the streamlined design of real vehicle. For more accurate matching, the fitting of the important wireframes is underlined in both two fitness functions. In the first fitness function, the larger weight coefficient is assigned to the fitting of the important wireframes; in the second fitness function, two different functions are used for the fitting of the model's wireframes instead of two different weight coefficients. Experiments on real traffic videos verify the correctness and robustness of the proposed fitness functions.

목차

Abstract
 1. Introduction
  1.1. Related Work
  1.2. Motivation
 2. Vehicle Wire-frame Model and its Projection
 3. Fitness Function
 4. Experiments
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Yuan Zheng [ Institute of Automation, Chinese Academy of Sciences, Beijing, China ]
• Silong Peng [ Institute of Automation, Chinese Academy of Sciences, Beijing, 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