基于DCT变换的多通道特征的行人检测

在行人检测中,针对目前多通道检测算法特征利用不充分的问题,提出一种基于DCT变换的多通道特征级联的行人检测算法。通过一种2层卷积网络模型将图像信息DCT变换后的数据进行整理,形成新的频域通道特征,该通道能描述行人的复杂纹理特征。结合梯度方向直方图特征、颜色空间特征和DCT频域特征,基于Adaboost算法训练了低开销的多通道特征行人检测器。在典型的公开行人库上的实验结果表明,该方法能提高检测的性能,在较低误检率时效果更加显著。

Pedestrian Detection Based on DCT of Multi-channel Feature

In pedestrian detection,multi-channel feature detection has the defect of incomplete using features.In this paper,an algorithm of multi-channel feature detection based on discrete cosine transform (DCT) was proposed.We used a two-layer convolution network for arranging the image information after DCT to build a new channel in the frequency domain.This channel can describe complex textures about pedestrian.Combined with the features of the histogram of gradient and the color space as well as the DCT frequency domain,a low cost multi-channel pedestrian detector has been trained based on the Adaboost algorithm.Experiments on popular pedestrian databases show that the proposed method improves the accuracy of detection,and the effect is remarkable in low false positive per image.