Vehicle logo recognition in traffic images using HOG features and SVM

Author

Llorca, D.F. ; Arroyo, R. ; Sotelo, M.A.

Author_Institution

Comput. Eng. Dept., Univ. of Alcala, Madrid, Spain

fYear

2013

fDate

6-9 Oct. 2013

Firstpage

2229

Lastpage

2234

Abstract

In this paper a new vehicle logo recognition approach is presented using Histograms of Oriented Gradients (HOG) and Support Vector Machines (SVM). The system is specifically devised to work with images supplied by traffic cameras where the logos appear with low resolution. A sliding-window technique combined with a majority voting scheme are used to provide the estimated car manufacturer. The proposed approach is assessed on a set of 3.579 vehicle images, captured by two different traffic cameras that belong to 27 distinctive vehicle manufacturers. The reported results show an overall recognition rate of 92.59%, which supports the use of the system for real applications.

Keywords

image recognition; road traffic; road vehicles; support vector machines; traffic engineering computing; video cameras; HOG features; SVM; estimated car manufacturer; histograms of oriented gradients; majority voting scheme; sliding-window technique; support vector machines; traffic cameras; traffic images; vehicle logo recognition; vehicle manufacturers; Accuracy; Cameras; Image recognition; Licenses; Support vector machines; Training; Vehicles; HOG features; SVM; Traffic Images; Vehicle Logo Recognition; Vehicle Manufacturer Recognition; majority vote; sliding window;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on

Conference_Location

The Hague

Type

conf

DOI

10.1109/ITSC.2013.6728559

Filename

6728559