Intelligent Real-Time Exam Surveillance and Anomaly Detection Using Deep Learning with Pose-Aware Behavioral Analysis
Keywords:
Behavior Analysis, ByteTrack, Exam Invigilation, Pose Estimation, Suspicion Scoring, YOLO11Abstract
Maintaining academic integrity in examination halls is a key priority for educational institutions, yet traditional invigilation remains resource-intensive, subject to cognitive fatigue, and prone to oversight. While automated solutions have been proposed, they frequently rely on simple bounding box classifications that fail to capture the subtle, temporal postures associated with cheating. This paper presents an intelligent, real-time exam surveillance framework that integrates object detection, multi-object tracking, and pose-aware behavioral analysis into a unified, edge-deployable pipeline. The proposed system utilizes a fine-tuned YOLO11 Nano network to isolate students and unauthorized objects, a ByteTrack multi-object tracker to maintain persistent student identities across frames, and a secondary YOLO11-pose estimation model to extract seventeen anatomical keypoints in real time. A geometric behavior analysis engine computes head turn deflections, lateral body leaning ratios, and arm reaches based on anatomical joint vectors, passing these inputs into an exponential-decay suspicion scoring engine to filter out innocent, transient movements. Deployed as a lightweight Flask application with a real-time WebSocket dashboard, the system achieves an overall classification accuracy of 96.2 %, with an empirical precision of 89.5 %, representing a significant improvement over baseline YOLOv8 bounding-box models that suffer from excessive false alerts.
References
R. Mulenga and H. Shilongo, “Academic Integrity in Higher Education: Understanding and Addressing Plagiarism,” Acta Pedagogia Asiana, vol. 3, no. 1, pp. 30–43, Jan. 2024.
M. Adil, R. Simon and S. K. Khatri, "Automated Invigilation System for Detection of Suspicious Activities during Examination," 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 2019, pp. 361-366.
N. Sharma, “Artificial Intelligence: Paving the Way to a Smarter Education System,” Intelligent Systems Reference Library, pp. 141–157, Jan. 2023.
P. Baile, N. Sutar, and S. Shinde, “A Survey on Suspicious Activity Detection in Examination Hall,” Journal of Pharmaceutical Negative Results, pp. 7565–7571, 2022.
D. Woldeab and T. Brothen, “Video Surveillance of Online Exam Proctoring: Exam Anxiety and Student Performance,” International Journal of E-Learning & Distance Education, vol. 36, no. 1, Jul. 2021.
T. S. Vally, M. A. Sofi, M. Mudiyala, S. S. Bakki, and P. Meghana, “Automated Invigilation System for Offline Exam Monitoring Using Deep Learning,” 2024 International Conference on Data Science and Network Security (ICDSNS), pp. 1–6, Jul. 2024.
M. A. Elah Abbas and S. Hameed, “A Systematic Review of Deep Learning Based Online Exam Proctoring Systems for Abnormal Student Behaviour Detection,” International Journal of Scientific Research in Science, Engineering and Technology, pp. 192–209, Jul. 2022.
S. Satre, S. Patil, T. Mane, V. Molawade, T. Gawand, and A. Mishra, “Online Exam Proctoring System Based on Artificial Intelligence,” 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), pp. 1–6, May 2023.
Z. Trabelsi, M. M. Ambali Parambil; and F. Alnajjar, “Behavioral-based real-time cheating detection in academic exams using deep learning techniques,” Etltc-Icetm2023 International Conference Proceedings: Ict Integration In Technical Education & Entertainment Technologies and Management, 2023.
M. D. Genemo, “Suspicious activity recognition for monitoring cheating in exams,” Proceedings of the Indian National Science Academy, vol. 88, no. 1, pp. 1–10, Feb. 2022.
M. R. Borse, V. Rathi, and rasad Gajanan Pawar, “Detecting Abnormal Behavior in Examination Surveillance with 3D Convolutional Neural Networks,” International Journal of Research in Engineering, Science and Management, 2021.
F. Mahmood et al., “Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms,” Sensors, vol. 22, no. 17, p. 6389, Jan. 2022.
M. J. Hoque, Md. Razu, Md. Jashim, and M. Mostafa, “Automation of Traditional Exam Invigilation using CCTV and Bio-Metric,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, 2020.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 3, no. 3, pp. 779–788, 2016.
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1680–1716, Dec. 2023.
