Collision warning remains an active research field due to the increasing complexities of on-road traffic worldwide. Vision-based warning systems are of particular interest because of the extensive information contained in images. This paper proposes the combination of Legendre moments and Gabor features for monocular vision-based vehicle recognition. We focus on vehicle recognition within a region of interest (ROI) in an image by assuming that the ROI has been detected by a radar sensor. Two classifiers including a support vector machine (SVM) and a neural network have been investigated to verify the effectiveness of the features. We have tested the proposed approaches on real-world video sequences acquired under various weather conditions for a wide range of vehicles and non-vehicles at up to 70 meters. The proposed combination of Legendre moments and Gabor features has yielded a correct classification rate of 99.1% and a false alarm rate of 1.9%. We have compared the proposed features with the over-complete Haar wavelets in the literature.
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