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    DEVELOPMENT OF A PAIRING-ANOMALY DETECTION SYSTEM IN VIDEO SURVEILLANCE USING OBJECT DETECTION MODEL
    (2025-03) SODIPO, QUEEN BUSAYO; Covenant University Diseertation
    Anomaly detection in social behaviours among students is critical to maintaining a safe and respectful academic environment. This research focuses on developing an object detection-based pairing-anomaly detection system to identify and classify unusual social behaviours, such as hand-holding, which can be perceived as an anomaly and potentially lead to more serious issues like sexual misconduct. Object detection, a most significant branch of computer vision, attempts to locate and identify objects in images or video frames. The other is anomaly detection, identifying data sets that do not follow expected behaviour configurations. Combining object detection and anomaly detection approaches provides a powerful solution to detect and flag anomalous or problematic behaviour in any setting. Object detection-based models have recently gained significant attention for their efficiency in detecting and identifying anomalies of interest in complex scenes at high precision. This study shows the application of deep learning for anomalous event detection with an object detection architecture. Utilising the YOLO (You Only Look Once) architecture, the system is designed to detect and localise anomalies in real-time. The model is trained on a custom holding hands dataset, specifically designed to capture instances of hand-holding alongside the Pascal VOC (Visual Object Classes) benchmark dataset, to ensure versatility across varied scenarios. This approach incorporates transfer learning and data augmentation techniques to and optimise model performance with limited labelled data. Evaluation metrics, including mean accuracy precision (mAP), recall, F1 score, and AUC (Area Under Curve), demonstrate the model's effectiveness, with the Custom Holding Hands Dataset achieving an impressive mAP score of 99.5%. The system is integrated into a web application, enabling real-time anomaly detection and classification. This research contributes to developing computer vision-based pairing-anomaly detection systems for social behaviour analysis, with potential applications in maintaining a safe and respectful academic environment.