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vision based home security system using support vector machine

Nivetha. R,Panimalar. B,R. K. Selvakumar

Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology

ISSN: 2321-3337          Impact Factor:1.521         Volume:4         Issue:3         Year: 01 April,2016         Pages:730-737

International Journal of Advanced Research in Computer Science Engineering and Information Technology

Abstract

Home security is one of the application in which image is processed in order to detect the human presence to protect ones asset from the housebreaker. Object detection technique is used along with classification to classify the human and non-human entity using SVM (Support Vector Machine) which is discriminative classifier. Before classifying the object, the image is enhanced; moving object is detected and then segmented. After that the boundary features of objected is extracted from the segmented image. These features are stored in a database. For classification of human and non-human entity such as cat and dog, we use SVM Classifier. It can be handled with both linear and non-linear classification and can classify the categories accurately. This solution is generic and can be used for various applications which require detection and classification of an object.

Kewords

Support vector machine, Histogram equalization, Canny edge detection, feature extraction, classification.

Reference

[1] A. W. D. Udaya Shalika, Lasantha Seneviratne, Animal Classification System Based on Image Processing & Support Vector Machine, Journal of Computer and Communications, 2016, 4, 12-21. [2] N. Elavarasan, Dr. K.Mani, A Survey on Feature Extraction Techniques, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 1, January 2015. [3] Bingquan Huo and Fengling Yin, Image Classification Algorithm based on Multi-Feature Extraction and Modified SVM Classifier, International Journal of Smart Home Vol. 9, No. 9 (2015). [4] T. Bouwmans, F. Porikli, B. Horferlin, A. Vacavant, Handbook on Background Modeling and Foreground Detection for Video Surveillance: Traditional and Recent Approaches, Implementations, Benchmarking and Evaluation, CRC Press, Taylor and Francis Group, June 2014. [5] Harshad Kadu and C.-C. Jay Kuo, Automatic Human Mocap Data Classification, ieee transactions on multimedia, vol. 16, no. 8, december 2014