文章基本信息

标题：HOUSEBREAKING CRIME GAIT PATTERN CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE
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作者：HANA ABD RAZAK ; ALI ABD ALMISREB ; MOHAMMED AHMED MOHAMMED SALEH 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：12
页码：2185-2198
出版社：Journal of Theoretical and Applied
摘要：The rate of crime is worsen and has led to a growing number of studies on human identification namely gait recognition. Hence, this study focused on the normal and anomalous behavior at the gate of residential units based on gait features extracted using Kinect sensor. Firstly, dataset of housebreaking crime behavior and normal behavior at the gate is acquired and collected. Further, orthogonal least squares (OLS) are utilized to extract and select the gait features along with principal component analysis (PCA) as gait feature optimization. Next, classification of gait features is done using artificial neural network (ANN) and support vector machine (SVM). Result attained showed that the recognition performance using ANN classifier was up to 99% but only 50% for SVM classifier. Findings from this study showed that the most optimum accuracy rate is at 99.78% using ANN with GDX as the learning algorithm in classifying both normal and anomalous behavior at the residential gate units.
关键词：Anomalous Behavior;Kinect;Orthogonal Least Square (OLS);Principal Component Analysis (PCA);Artificial Neural Network (ANN);Support Vector Machine (SVM)