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王蒙,戴亚平.多传感器人体检测的FHOG图像特征融合[J].北京理工大学学报（自然科学版）,2015,35(2):192~196,202.
WANG Meng,DAI Ya-ping.Image Feature Fusion for Human Detection with Multi-Sensor Based on FHOG[J].Transactions of Beijing Institute of Technology,2015,35(2):192-196,202.

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多传感器人体检测的FHOG图像特征融合

Image Feature Fusion for Human Detection with Multi-Sensor Based on FHOG

投稿时间：2034-06-19

DOI：10.15918/j.tbit1001-0645.2015.02.016

中文关键词: 视觉激活度融合方向梯度直方图图像特征融合人体检测

English Keywords:visual active measurement (VAM) fused histogram of oriented gradient (FHOG) image feature fusion human detection

基金项目:

作者	单位
王蒙	北京理工大学自动化学院, 北京 100081 大理学院数学与计算机学院, 云南, 大理 671003
戴亚平	北京理工大学自动化学院, 北京 100081

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中文摘要:

提出了一种新的基于方向梯度直方图(HOG)的图像特征融合方法. 该方法采用视觉激活度(VAM)来选择具有显著方向性的局部梯度统计值,构成融合的方向梯度直方图(FHOG),有效地解决了多分辨率(MR)图像融合存在的不足. 文中把这些融合特征输入线性支持向量机(SVM),训练得到人体/背景二元分类器用于人体检测. 实验表明,与传统多分辨率图像融合方法相比,在参考点处本文提出方法漏检率下降3～10%,虚警率平均下降20%以上.

English Summary:

Proposed a novel image feature fusion approach based on histogram of oriented gradient (HOG). The visual activation measure (VAM) was used to select the statistics of local gradients with significant direction, and forms fused histogram of gradient (FHOG), which effectively solved the existing deficiencies of multi-resolution (MR) image fusion. These fused features were plugged into support vector machine (SVM), and train human/background binary classifier for human detection. Experiments show that compared with the traditional MR image fusion approaches, in reference points the missing rate of the proposed approach decreases by 3%～10%, and the false alarm rate drops by an average of more than 20%.

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