一种基于多特征和机器学习的分级行人检测方法

种衍文; 匡湖林; 李清泉

doi:10.3724/SP.J.1004.2012.00375

一种基于多特征和机器学习的分级行人检测方法

doi: 10.3724/SP.J.1004.2012.00375

1.
武汉大学测绘遥感信息工程国家重点实验室武汉 430079

详细信息

通讯作者:
种衍文, 武汉大学测绘遥感信息工程国家重点实验室副研究员. 主要研究方向为计算机视觉,数字视频处理和模式识别. E-mail: apollobest@126.com

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出版历程
- 收稿日期: 2011-07-11
- 修回日期: 2011-09-14
- 刊出日期: 2012-03-20

Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079

摘要

摘要: 针对单幅图像中的行人检测问题,提出了基于自适应增强算法(Adaboost)和支持向量机(Support vector machine, SVM)的两级检测方法, 应用粗细结合的思想有效提高检测的精度.粗级行人检测器通过提取四方向特征(Four direction features, FDF)和GAB (Gentle Adaboost)级联训练得到,精密级行人检测器用熵梯度直方图(Entropy-histograms of oriented gradients, EHOG)作为特征, 通过支持向量机学习得到.本文提出的EHOG特征考虑到熵, 通过分布的混乱程度描述,具有分辨行人和类似人的物体能力. 实验结果表明,本文提出的EHOG、粗细结合的两级检测方法能准确地检测出复杂背景下不同姿势的直立行人, 检测精度优于以往Adaboost方法.
- 四方向特征 /
- 熵梯度直方图 /
- 自适应增强算法 /
- GAB级联 /
- 支持向量机 /
- 两级检测
Abstract: A two-stage detection method based on Adaboost and support vector machine (SVM) is proposed for the pedestrian detection problem in a single image, which uses the combination of coarse level and fine level detection to improve the accuracy of the detector. The coarse level pedestrian detector makes use of the four direction features (FDF) and the gentle Adaboost (GAB) cascade training; the fine level pedestrian detector uses entropy-histograms of oriented gradients (EHOG) as features and the SVM as classifier. The proposed EHOG features considering entropy and the distribution of chaos have the ability to distinguish between the pedestrians and the objects similar to people. Experimental results show that the proposed two-stage pedestrian detection method with the combination of the coarse-fine level and EHOG feature can accurately detect upright bodies with different postures in the complex background, at the same time the precision is better than the classic Adaboost methods.
- Four direction features (FDF) /
- entropy-histograms of oriented gradients (EHOG) /
- Adaboost /
- gentle Adaboost (GAB) cascade /
- support vector machine (SVM) /
- two-stage detection

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参考文献(1)

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