Adaptive feature annotation for large video sensor networks

Yang Cai; Andrew Bunn; Peter Liang; Bing Yang

doi:10.1117/1.JEI.22.4.041110

6 September 2013 Adaptive feature annotation for large video sensor networks

Yang Cai, Andrew Bunn, Peter Liang, Bing Yang

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 041110 (September 2013). https://doi.org/10.1117/1.JEI.22.4.041110

Abstract

We present an adaptive feature extraction and annotation algorithm for articulating traffic events from surveillance cameras. We use approximate median filter for moving object detection, motion energy image and convex hull for lane detection, and adaptive proportion models for vehicle classification. It is found that our approach outperforms three-dimensional modeling and scale-independent feature transformation algorithms in terms of robustness. The multiresolution-based video codec algorithm enables a quality-of-service-aware video streaming according to the data traffic. Furthermore, our empirical data shows that it is feasible to use the metadata to facilitate the real-time communication between an infrastructure and a vehicle for safer and more efficient traffic control.

Citation Download Citation

Yang Cai, Andrew Bunn, Peter Liang, and Bing Yang "Adaptive feature annotation for large video sensor networks," Journal of Electronic Imaging 22(4), 041110 (6 September 2013). https://doi.org/10.1117/1.JEI.22.4.041110

Published: 6 September 2013

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