基于显著性检测与HOG-NMF特征的快速行人检测方法

Fast Pedestrian Detection Based on Saliency Detection and HOG-NMF Features

行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用频谱调谐显著性检测提取显著图,并基于熵值门限进行感兴趣区域的提取;组合非负矩阵分解和方向梯度直方图生成HOG-NMF特征;采用加性交叉核支持向量机方法(Intersection Kernel Support Vector Machine,IKSVM)。该算法显著降低了特征维数,在相同的计算复杂度下明显改善了线性支持向量机的检测率。在INRIA数据库的实验结果表明,该方法对比HOG/线性SVM和HOG/RBF-SVM显著减少了检测时间,并达到了满意的检测率。

Pedestrian detection is a key ability for a variety of important applications,such as robotics,driver assistance systems and surveillance.This paper presents a fast pedestrian detection based on saliency detection and Histogram of Oriented Gradient-Non-negative Matrix Factorization（HOG-NMF） features.The regions of interest are extracted using the frequency tuned saliency detection and threshold based on entropy.A novel HOG-NMF features that reduce significantly the length of feature vector are proposed.Classification method using intersection kernel SVM offers significant improvements in accuracy over linear SVM with the same computational complexity.Experiments on INRIA dataset show that the proposed method reduces significantly runtime compared with HOG/linear SVM and HOG/RBF-SVM,achieves the satisfactory accuracy.