Gabor特征结合快速HOG特征的行人检测

针对行人检测中HOG特征提取速度慢且易忽视细节特征的问题,提出了一种Gabor特征结合快速HOG特征的行人检测算法.首先对输入图像进行小波变换,并引入积分图思想和主成分分析算法快速提取图像HOG特征;其次融合Gabor小波变换得到的Gabor特征,最后采用混合特征训练分类器,实现行人的有效检测.测试集上的实验结果表明,在使用相同分类器的情况下,该混合特征提取方法比单一特征提取方法的检测正确率最多可提高7.37％,因此所提出的算法可以有效地提高行人检测的精度.

Pedestrian Detection Based on Gabor Feature Combined with Fast HOG Feature

Histogram of Oriented Gradients (HOG) feature extraction has a slow speed and is prone to the omission of detailed features in pedestrian detection. To tackle these problems, this study proposes a novel pedestrian detection algorithm based on Gabor feature combined with fast HOG feature. Specifically, the input image is first subjected to wavelet transform and the HOG feature of the image is quickly extracted using the idea of integral image and the principal component analysis algorithm. Then the fast HOG feature is fused with the Gabor feature obtained after Gabor wavelet transform. Finally, the hybrid features are used to train the classifier for effective pedestrian detection. The experimental results on the test set show that the detection accuracy of the hybrid feature extraction method is up to 7.37% higher than that of the single feature extraction method when the same classifier is used. Therefore, the proposed algorithm can effectively improve the accuracy of pedestrian detection.