Efficient Feature Selection and Classification for Vehicle Detection

The focus of this work is on the problem of Haar-like feature selection and classification for vehicle detection. Haar-like features are particularly attractive for vehicle detection because they form a compact representation, encode edge and structural information, capture information from multiple scales, and especially can be computed efficiently. Due to the large-scale nature of the Haar-like feature pool, we present a rapid and effective feature selection method via AdaBoost by combining a sample’s feature value with its class label. Our approach is analyzed theoretically and empirically to show its efficiency. Then an improved normalization algorithm for the selected feature values is designed to reduce the intra-class difference while increasing the inter-class variability. Experimental results demonstrate that the proposed approaches not only speed up the feature selection process with AdaBoost but also yield better detection performance than the state-of-the-art methods.