Occlusion detection for highway traffic analysis with vision-based surveillance systems

Akio Yoneyama; Chia-Hung Yeh; C.-C. Jay Jay Kuo

doi:10.1117/12.487294

8 August 2003 Occlusion detection for highway traffic analysis with vision-based surveillance systems

Akio Yoneyama, Chia-Hung Yeh, C.-C. Jay Jay Kuo

Proceedings Volume 5108, Visual Information Processing XII; (2003) https://doi.org/10.1117/12.487294
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

The vision-based traffic monitoring system provides an attractive solution in extracting various traffic parameters such as the count, speed, flow and concentration from the processing of video data captured by a camera system. The detection accuracy is however affected by various environment factors such as shadow, occlusion, and lighting. Among these, the occurrence of occlusion is one of the major problems. In this work, a new scheme is proposed to detect the occlusion and determine the exact location of each vehicle. The proposed algorithm is based on the matching of images from multiple cameras. In the proposed scheme, we do not need edge detection, region segmentation, and camera calibration operations, which often suffer from the variation of environmental conditions. Experimental results are given to verify that the proposed technique is effective for vision-based highway surveillance systems.

Citation Download Citation

Akio Yoneyama, Chia-Hung Yeh, and C.-C. Jay Jay Kuo "Occlusion detection for highway traffic analysis with vision-based surveillance systems", Proc. SPIE 5108, Visual Information Processing XII, (8 August 2003); https://doi.org/10.1117/12.487294

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