计算机应用与软件
計算機應用與軟件
계산궤응용여연건
Computer Applications and Software
2015年
9期
170-174
,共5页
聚集型B-Haar%Edgelet特征%双层结构%行人识别%贝叶斯原理
聚集型B-Haar%Edgelet特徵%雙層結構%行人識彆%貝葉斯原理
취집형B-Haar%Edgelet특정%쌍층결구%행인식별%패협사원리
Aggregated%B-Haar%Edgelet feature%Double-%layer structure%Pedestrian recognition%Bayesian principle
在对公共场所人流量统计的过程中，为了有效解决因行人遮挡、粘连所引发的在行人识别上的低检测率、高虚警率、实时性不足的缺点，对聚集型B-Haar特征和Edgelet特征协调进行特征提取，设计了双层组合结构行人识别模型。该模型的上层是在完全二叉树架构下结合局部二元模式改进的Haar特征（称作聚集型B-Haar特征），主管提取候选行人目标，确保较高的检测识别率；下层树状结构使用四分支串联树状结构，利用Edgelet特征并结合贝叶斯原理构建树状决策结构，对候选行人多部位检测然后判断候选目标是否为行人，实现降低虚警概率，保证实时性的目标。经过实验分析表明，所设计的多特征协同双层组合结构行人识别方法与传统的树状结构、串并联结构相比，在实时性、检测率和虚警率上具有明显的整体优势。
在對公共場所人流量統計的過程中，為瞭有效解決因行人遮擋、粘連所引髮的在行人識彆上的低檢測率、高虛警率、實時性不足的缺點，對聚集型B-Haar特徵和Edgelet特徵協調進行特徵提取，設計瞭雙層組閤結構行人識彆模型。該模型的上層是在完全二扠樹架構下結閤跼部二元模式改進的Haar特徵（稱作聚集型B-Haar特徵），主管提取候選行人目標，確保較高的檢測識彆率；下層樹狀結構使用四分支串聯樹狀結構，利用Edgelet特徵併結閤貝葉斯原理構建樹狀決策結構，對候選行人多部位檢測然後判斷候選目標是否為行人，實現降低虛警概率，保證實時性的目標。經過實驗分析錶明，所設計的多特徵協同雙層組閤結構行人識彆方法與傳統的樹狀結構、串併聯結構相比，在實時性、檢測率和虛警率上具有明顯的整體優勢。
재대공공장소인류량통계적과정중，위료유효해결인행인차당、점련소인발적재행인식별상적저검측솔、고허경솔、실시성불족적결점，대취집형B-Haar특정화Edgelet특정협조진행특정제취，설계료쌍층조합결구행인식별모형。해모형적상층시재완전이차수가구하결합국부이원모식개진적Haar특정（칭작취집형B-Haar특정），주관제취후선행인목표，학보교고적검측식별솔；하층수상결구사용사분지천련수상결구，이용Edgelet특정병결합패협사원리구건수상결책결구，대후선행인다부위검측연후판단후선목표시부위행인，실현강저허경개솔，보증실시성적목표。경과실험분석표명，소설계적다특정협동쌍층조합결구행인식별방법여전통적수상결구、천병련결구상비，재실시성、검측솔화허경솔상구유명현적정체우세。
In the process of human traffic statistics in public places,in order to effectively handle the shortcoming of low detection rate, high false alarm rate and poor real-time performance in pedestrian recognition caused by pedestrian occlusion and adhesion,we perform the features extraction of the aggregated B-Haar and the Edgelet feature coordination,design a pedestrian recognition model with double-layer composite structure.The upper layer of the model is a Haar feature (referred to as aggregated B-Haar feature)improved in combination with local binary pattern in complete binary tree structure,in charge of the extraction of candidate pedestrian targets,and makes sure the higher detecting recognition rate.The tree structure in lower layer contains a four-branch tree structure in series,it uses Edgelet feature and combining Bayesian principle to construct tree decision-making structure,carries out multi-position detection on candidate target and then determines whether the target is a pedestrian or not,thus achieves the aim of lowering false alarm rate and ensuring real-time performance.It is revealed by experimental analysis that compare with traditional tree structure and serial-parallel structure,the multi-feature collaboration pedestrian recognition algorithm with double-layer composite structure designed in this paper has obvious overall advantages in real-time performance,detection rate and false alarm rate.
