Efficient Feature Selection and Classification for Vehicle Detection
Abstract— The focus of this paper 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 < Final Year Projects 2016 > temporarily to show its efﬁciency. Then, an improved normalization algorithm for the selectedfeature values is designed to reduce the intra-class difference, while increasing the inter-class variability.