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The IUP Journal of Telecommunications

Recognizing Suspicious Activities in Examination Scenario Using Machine Learning Techniques

Article Details

Pub. Date	: November' 2021
Product Name	: The IUP Journal of Telecommunications
Product Type	: Article
Product Code	: IJTC41121
Author Name	: Agwi Uche Celestine, Ogwueleka Francisca N* and Irhebhude Martins Ekata***
Availability	: YES
Subject/Domain	: Arts & Humanities
Download Format	: PDF Format
No. of Pages	: 15

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Abstract

Monitoring the activities of examinees during examination is very challenging. The paper recognizes and classifies activities of examinees as suspicious or normal during examination using machine learning techniques. The processing and analysis of image data follows a typical sequence of distinct steps referred to as the vision pipeline. Data was acquired with a surveillance camera and frames extracted from the videos. Preprocessing activities include selecting the required frames from frame sequences, and cropping and segmenting foreground/background object. Video conversion to frame was accomplished with MATLAB scripts, while segmentation of image frames was achieved with GrabCut algorithm. Shape/pose features were extracted from objects using Histogram of Oriented Gradient (HOG) and Regionprop algorithms, and represented in feature vectors that were fed into Support Vector Machine (SVM) classifier. Holdout validation technique was used for the classifier training and tested from the given datasets. 70% of the dataset was used for training, while 30% was used for testing. The model gave an accuracy of 98.1% and 100%, respectively, for each examination scenario. The model accuracy was visualized in confusion matrix and the Receiver Operating Characteristic (ROC). MATLAB software was used as the simulation environment. The model demonstrated excellent performance, indicating that the system can adequately complement the efforts of invigilators in examination invigilation.

Introduction

Technology such as machine learning helps in solving a lot of problems across several application domains, including entertainment, healthcare and surveillance (Ke et al., 2013). The interest in human activity monitoring, recognition and understanding through a surveillance system has increased over time and is gaining more attention as the concern for proactive information about safety and security grows.

Keywords:

Examination malpractice, Suspicious activities, Closed-Circuit Television (CCTV) camera, GrabCut, Support Vector Machine (SVM), Receiver Operating Characteristic (ROC), Histogram of Oriented Gradient (HOG)

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