Special Section on Video Surveillance and Transportation Imaging Applications

Vehicle classification in pan-tilt-zoom videos via sparse learning

[+] Author Affiliations
Cong Wu

Nanjing University, School of Electronic Science and Engineering, P.O.1123 210093, China

Bo Li

Nanjing University, School of Electronic Science and Engineering, P.O.1123 210093, China

Shu Shen

Nanjing University, School of Electronic Science and Engineering, P.O.1123 210093, China

Qimei Chen

Nanjing University, School of Electronic Science and Engineering, P.O.1123 210093, China

J. Electron. Imaging. 22(4), 041102 (Jun 11, 2013). doi:10.1117/1.JEI.22.4.041102
History: Received June 12, 2012; Revised September 26, 2012; Accepted November 16, 2012
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Abstract.  We design and implement a robust vehicle classification system based on pan-tilt-zoom cameras. We introduce a simple but effective camera-invariant feature to describe the intrinsic shape patterns of vehicles. The introduced feature can be directly extracted from two-dimensional images, eliminating the need for complicated three-dimensional template fitting used in existing vehicle classification systems. Also, we introduce a prevalent sparse model to make the discriminative learning procedures robust to noise. Experimental results on practical highways show that the proposed system could achieve promising results on vehicle classification in real time.

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Citation

Cong Wu ; Bo Li ; Shu Shen and Qimei Chen
"Vehicle classification in pan-tilt-zoom videos via sparse learning", J. Electron. Imaging. 22(4), 041102 (Jun 11, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041102


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