การตรวจจับพฤติกรรมต้องสงสัยระหว่างสอบออนไลน์ด้วยตัวกรองอนุภาคแบบราวแบล็คเวลไลซ์
Keywords:
image processing, human behaviour, human action, rao-blackwellized particle filterAbstract
Online exams have become widely used to evaluate students’ performance in recent years. The ability to efficiently proctor is an important limiting for online exams due to the lack of face to face interaction. Prior research in computer vision has shown that online exams are more vulnerable to various cheating behaviors. The problem is challenging due to its various behavior activities in examination or by monitoring them. There are many algorithms attempt to analyze and detect in human behavior activities. But techniques are the limits in various conditions. To overcome this problem, this paper proposes a detection of suspicious human behavior with Rao-blackwellized particle filter. The proposed model extracts the keypoint features in an automated way with OpenPose. This paper presents main three steps including (1) data preprocessing, (2) detect behavior with Roa-blackwellized particle filter, and (3) efficiency measurement and evaluation of experimental results. The experimental results indicate that our framework offers performance improvements. The proposed model can achieve significant improvements for behaviour detection with maximum success rate of 79.63% with data set 2. The experimental results indicate that our proposed approach offers significant performance improvements in the detection of suspicious human behaviour.
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