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Journal of Electronic Imaging

Adaptive feature annotation for large video sensor networks
Author(s): Yang Cai; Andrew Bunn; Peter Liang; Bing Yang
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Paper 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.

Paper Details

Date Published: 6 September 2013
PDF: 10 pages
J. Electron. Imag. 22(4) 041110 doi: 10.1117/1.JEI.22.4.041110
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Yang Cai, Carnegie Mellon Univ. (United States)
Andrew Bunn, Carnegie Mellon Univ. (United States)
Peter Liang, Carnegie Mellon Univ. (United States)
Bing Yang, Carnegie Mellon Univ. (United States)

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