摘要
车载红外行人检测在准确率和实时性方面存在多方挑战.文中基于行人头部、躯干成像与背景之间存在灰度分布差异,构建行人头部模型和躯干模型作为前端分类器,后端采用支持向量机(Support Vector Machine,SVM)进行分类;结合多帧校验和最近邻匹配跟踪行人.实验结果表明,检测时间基本持平,提高了检测准确率.
There are lots of challenges in terms of precision and real-time performance in the detection of vehicular infrared pedestrian. This article established the pedestrians＇ head and torso models as the frond-end classifiers based on the brightness distribution difference between the pedestrians＇ head,torso and the background,and adopted the support vector machine（ SVM） as the rear-end classifier; multi-frame check and nearest matching were combined to track the pedestrians.Experiment results showthat the detection time is basically unchanged,and the detection accuracy have been improved.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第7期1444-1448,共5页
Acta Electronica Sinica
基金
国家自然科学基金(No.61302121)
关键词
红外视频
行人检测
头部模型
躯干模型
行人跟踪
infrared video
pedestrian detection
head model
torso model
pedestrian tracking
