Special Section on Video Surveillance and Transportation Imaging Applications

Adaptive feature annotation for large video sensor networks

[+] Author Affiliations
Yang Cai

Carnegie Mellon University, Cylab, CIC Building 2218, 4720 Forbes Avenue, Pittsburgh, Pennsylvania 15213

Andrew Bunn

Carnegie Mellon University, Cylab, CIC Building 2218, 4720 Forbes Avenue, Pittsburgh, Pennsylvania 15213

Peter Liang

Carnegie Mellon University, Cylab, CIC Building 2218, 4720 Forbes Avenue, Pittsburgh, Pennsylvania 15213

Bing Yang

Carnegie Mellon University, Cylab, CIC Building 2218, 4720 Forbes Avenue, Pittsburgh, Pennsylvania 15213

J. Electron. Imaging. 22(4), 041110 (Sep 06, 2013). doi:10.1117/1.JEI.22.4.041110
History: Received April 15, 2013; Revised July 2, 2013; Accepted July 17, 2013
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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.

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Citation

Yang Cai ; Andrew Bunn ; Peter Liang and Bing Yang
"Adaptive feature annotation for large video sensor networks", J. Electron. Imaging. 22(4), 041110 (Sep 06, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041110


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