Quantized wavelet features and support vector machines for on-road vehicle detection

The focus of this work is on the problem of feature selection and classification for on-road vehicle detection. In particular, we propose using quantized Haar wavelet features and Support Vector Machines (SVMs) for rear-view vehicle detection. Wavelet features are particularly attractive for vehicle detection because they form a compact representation, encode edge information, capture information from multiple scales, and can be computed efficiently. Traditionally, methods using wavelet features for classification truncate the coefficients by keeping only the ones with largest magnitude. We believe that the actual values of the wavelet coefficients are not very important for vehicle detection. In fact, the coefficient magnitudes indicate local oriented intensity differences, information that could be very different even for the same vehicle under different lighting conditions. Therefore, we argue and demonstrate experimentally that the actual coefficient values are less important compared to the simple presence or absence of those coefficients. Specifically, we propose quantizing large negative coefficients to -1, large positive coefficients to 1, and setting the rest coefficients to 0. The quantized coefficients seem to encode important information about the general shape and structure of vehicles, while ignoring fine details and allowing for sufficient inter-class variability. Experimental results and comparisons using real data demonstrate the superiority of the proposed approach which has achieved an average accuracy of 93.94% on completely novel test images.