Vehicle Detection Using Partial Least Squares

TitleVehicle Detection Using Partial Least Squares
Publication TypeJournal Articles
Year of Publication2011
AuthorsKembhavi A, Harwood D, Davis LS
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination1250 - 1265
Date Published2011/06//
ISBN Number0162-8828
Keywordsaerial images;color probability maps;oriented gradients feature;partial least squares;powerful feature selection analysis;urban planning;vehicle detection;visual surveillance;gradient methods;image colour analysis;least squares approximations;road vehicle, Automated;

Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and their surroundings, along with the Histograms of Oriented Gradients feature and a simple yet powerful image descriptor that captures the structural characteristics of objects named Pairs of Pixels. The combination of these features leads to an extremely high-dimensional feature set (approximately 70,000 elements). Partial Least Squares is first used to project the data onto a much lower dimensional subspace. Then, a powerful feature selection analysis is employed to improve the performance while vastly reducing the number of features that must be calculated. We compare our system to previous approaches on two challenging data sets and show superior performance.