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

Cong Wu; Bo Li; Shu Shen; Qimei Chen

doi:10.1117/1.JEI.22.4.041102

11 June 2013 Vehicle classification in pan-tilt-zoom videos via sparse learning

Cong Wu, Bo Li, Shu Shen, Qimei Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 041102 (June 2013). https://doi.org/10.1117/1.JEI.22.4.041102

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.

Citation Download Citation

Cong Wu, Bo Li, Shu Shen, and Qimei Chen "Vehicle classification in pan-tilt-zoom videos via sparse learning," Journal of Electronic Imaging 22(4), 041102 (11 June 2013). https://doi.org/10.1117/1.JEI.22.4.041102

Published: 11 June 2013

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