基于复合分类特征的红外图像人体实时检测  

Hybrid Classification Features-based Real-time Pedestrian Detection in Far-infrared Images

作　　者：李建福[1,2] 龚卫国[1] 杨金妃[1] 李伟红[1] 

机构地区：[1]重庆大学光电技术及系统教育部重点实验室,重庆400030 [2]重庆教育学院计算机与现代教育技术系,重庆400067

出　　处：《光电工程》2009年第2期55-61,共7页Opto-Electronic Engineering

基　　金：国家“十一五”基础研究项目(C10020060355);国家863计划项目(2007AA1E243);重庆市科技攻关重点项目(CSTC2007AC2018);重庆市自然科学基金项目(CSTC2008BB2199)

摘　　要：针对红外图像中人体区域的检测问题,提出了一种基于复合分类特征的人体实时检测方法。首先根据红外图像中人体区域的特点,使用自适应的两级方向投影获得人体候选区域的可能位置,然后融合方向梯度直方图特征、人体形状特征及亮度分布惯性特征以充分描述人体区域的特点,并且采用支持向量机算法对候选目标中存在的人体进行分类检测。实验结果表明,本文提出的方法充分利用了复合分类特征各自的优点,具有较好的实时性和鲁棒性。An effective method for real-time pedestrian detection applied to far-infrared images is presented, which makes use of the characteristics of pedestrian regions in far-infrared images and is based on a hybrid classification features algorithm. First, two levels of statistical adaptive oriented projection methods based on the high brightness property of the pedestrian pixels are used to locate the Regions of Interest （ROI） and eliminate the ＂shadow＂ phenomenon caused by one level oriented projection method. Then the method combines the pedestrian＇s shape-dependent and shape-independent features （including shape＇s morphological feature, inertia-based feature and histograms of oriented gradients （HOG） feature） to describe the ROI in the round. Finally, Support Vector Machine （SVM） is applied to classify and detect the pedestrian region. Experimental results of several far-infrared image sequences show that the proposed method achieves highly accurate pedestrian detection by combining hybrid classification features, and can be employed in real-time applications.

关 键 词：人体检测 自适应方向投影 复合分类特征 红外图像 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]