Active visual tracking method self-adapting to illumination based on particle filter pre-location

Jie Su; Guisheng Yin; Zhenhua Wei; Yining Xie

doi:10.1117/12.839114

18 January 2010 Active visual tracking method self-adapting to illumination based on particle filter pre-location

Jie Su, Guisheng Yin, Zhenhua Wei, Yining Xie

Proceedings Volume 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques; 75390Z (2010) https://doi.org/10.1117/12.839114
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

To improve the identification rate and tracking rate for quickly moving target, expand tracking scope and lower the sensitivity to illumination varying, an active visual tracking system self-adapting to illumination based on particle filter pre-location is proposed. The algorithm of object pre-location based on particle filter is used to realize realtime tracking to moving target by forecasting its location and control camera joints of Tilt and Pan. The method resetting system is used to improve accuracy of system. Brightness histogram equalization method is used to reduce the affect of illuminating varying in pre-location algorithm. Experiments and property analysis show that the real-time and accuracy are greatly improved.

Citation Download Citation

Jie Su, Guisheng Yin, Zhenhua Wei, and Yining Xie "Active visual tracking method self-adapting to illumination based on particle filter pre-location", Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 75390Z (18 January 2010); https://doi.org/10.1117/12.839114

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available