Segmentation of moving object in complex environment

Yang Yong; Jingru Wang; Qiheng Zhang

doi:10.1117/12.570150

8 February 2005 Segmentation of moving object in complex environment

Yang Yong, Jingru Wang, Qiheng Zhang

Proceedings Volume 5637, Electronic Imaging and Multimedia Technology IV; (2005) https://doi.org/10.1117/12.570150
Event: Photonics Asia, 2004, Beijing, China

Abstract

This paper presents a new automatic image segmentation method for segmenting moving object in complex environment by combining the motion information with edge information. We propose an adaptive optical flow method based on the Horn-Schunck algorithm to estimate the optical flow field. Our method puts different smoothness constraints on different directions and optical flow constraint is used according to the gradient magnitude. Canny edge detector can obtain the most edge information but miss some pixels. In order to restore these missing pixels the edge has a growing based on the continuity of optical flow field. Next, by remaining the block that has the longest edge could delete the noise in the background, and then the last segmentation result is obtained. The experimental result demonstrates that this method can segment the moving object in complex environment precisely.

Citation Download Citation

Yang Yong, Jingru Wang, and Qiheng Zhang "Segmentation of moving object in complex environment", Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005); https://doi.org/10.1117/12.570150

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