Application of Support Vector Machine Classifier for Security Surveillance System

C.P. Tan, N.F.M. Lani, and W.K. Lai (Malaysia)


Support Vector Machine, classification, security surveillance


This paper presents the application of Support Vector Machine classifier for security surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the popularly used Support Vector Machine classifier is performed to focus on the need of detecting dangerous weapons. In this paper, we take advantage of the classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of Support Vector Machine classifier, several classifiers are used to compare the overall accuracy of the system. These classifiers include Neural Network, Decision Trees, Naïve Bayes and k-Nearest Neighbor methods. The final outcome of this research clearly indicates that Support Vector Machine has the ability in improving the classification accuracy using the extracted features.

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