Efficient Feature Selection and Classification for Vehicle Detection
Efficient Feature Selection and Classification for Vehicle Detection
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DOI：
10.1109/tcsvt.2014.2358031
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发表时间：
2015-03-01
影响因子：
8.4
通讯作者：
Xue, Yu
中科院分区：
文献类型：
--
作者：
Wen, Xuezhi;Shao, Ling;Xue, Yu
本文主要研究了用于车辆检测的类Haar特征选择和分类问题。Haar类特征对于车辆检测特别有吸引力，因为它们形成紧凑的表示，编码边缘和结构信息，从多个尺度捕获信息，并且特别是可以有效地计算。由于类Haar特征池的大规模性质，我们通过AdaBoost将样本的特征值与其类别标签相结合，提出了一种快速有效的特征选择方法。我们的方法进行了理论和实证分析，以显示其效率。然后，一种改进的归一化算法的选择的特征值被设计，以减少类内差异，同时增加类间的变化。实验结果表明，所提出的方法不仅加快了AdaBoost的特征选择过程，而且比现有方法具有更好的检测性能。
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 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.