Vehicle classification for road tunnel surveillance

Andrés Frías-Velázquez; Peter Van Hese; Aleksandra Pižurica; Wilfried Philips

doi:10.1117/12.2008190

19 March 2013 Vehicle classification for road tunnel surveillance

Andrés Frías-Velázquez, Peter Van Hese, Aleksandra Pižurica, Wilfried Philips

Proceedings Volume 8663, Video Surveillance and Transportation Imaging Applications; 86630M (2013) https://doi.org/10.1117/12.2008190
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Vehicle classification for tunnel surveillance aims to not only retrieve vehicle class statistics, but also prevent accidents by recognizing vehicles carrying dangerous goods. In this paper, we describe a method to classify vehicle images that experience different geometrical variations and challenging photometrical conditions such as those found in road tunnels. Unlike previous approaches, we propose a classification method that does not rely on the length and height estimation of the vehicles. Alternatively, we propose a novel descriptor based on trace transform signatures to extract salient and non-correlated information of the vehicle images. Also, we propose a metric that measures the complexity of the vehicles’ shape based on corner point detection. As a result, these features describe the vehicle’s appearance and shape complexity independently of the scale, pose, and illumination conditions. Experiments with vehicles captured from three different cameras confirm the saliency and robustness of the features proposed, achieving an overall accuracy of 97.5% for the classification of four different vehicle classes. For vehicles transporting dangerous goods, our classification scheme achieves an average recall of 97.6% at a precision of 98.6% for the combination of lorries and tankers, which is a very good result considering the scene conditions.

Citation Download Citation

Andrés Frías-Velázquez, Peter Van Hese, Aleksandra Pižurica, and Wilfried Philips "Vehicle classification for road tunnel surveillance", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 86630M (19 March 2013); https://doi.org/10.1117/12.2008190

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