CAJ | 학술논문

提出了一种固定场景视频序列异常检测和正常行为分类的方法.该方法定义行人正常的走路和跑动为正常行为,最大的特点在于时空联合特征的选择.首先选用区域特征,通过分析正常行为找到特征的在时间上的统计规律,视频序列中行人不符合规律的行为被判定为异常.然后选用具有时空联合分布特点的目标轮廓特征,通过支持向量机(Support Vector Machine,SVM)进行训练,在训练的基础上判断目标行为是走路还是跑动.该方法在一定样本基础上进行了实验,实验结果表明,该方法能够较好进行行为检测和分类,性能比其他方法也有提高.
제출료일충고정장경시빈서렬이상검측화정상행위분류적방법.해방법정의행인정상적주로화포동위정상행위,최대적특점재우시공연합특정적선택.수선선용구역특정,통과분석정상행위조도특정적재시간상적통계규률,시빈서렬중행인불부합규률적행위피판정위이상.연후선용구유시공연합분포특점적목표륜곽특정,통과지지향량궤(Support Vector Machine,SVM)진행훈련,재훈련적기출상판단목표행위시주로환시포동.해방법재일정양본기출상진행료실험,실험결과표명,해방법능구교호진행행위검측화분류,성능비기타방법야유제고.
A method is proposed for behavior abnormality detection and normal behavior classification of human in video sequence.In this method,the walk activity and the run activity are defined as normal behaviors,and the main innovative point is the selection of the spatio-temporal characters.Firstly,the region character is selected,so the statistical rule can be acquired by the normal behavior analysis.The behavior which can not accord with the rule is thought of abnormal.Then the outline character is selected too.These outline can describe the spatio-temporal distributing of the target.A behavior is classified into two categories (walk or run) by the training based on Support Vector Machine(SVM) .This method is experimented with some samples.The results show that this method can detect the abnormal behavior and classify the normal behavior.The efficiency is better than the other related works too.