Anomaly Detection in Video Surveillance Using YOLOv8

This paper presents a novel hybrid approach for real-time anomaly detection in video surveillance systems by integrating YOLOv8 object detection with advanced motion-based analysis techniques. The proposed system addresses critical limitations of existing single-modality detection methods through innovative fusion of deep learning and temporal analysis. The architecture incorporates parallel processing pipelines for YOLOv8 detection and optical flow computation, combined with an isolation forest-based anomaly decision framework that leverages historical detection patterns. Experimental evalution on a custom dataset of 5,000 surveillance video clips demonstrates superior performance with 92.3% accuracy, 89.7% precision, 94.1% recall, and 91.8% F1-score, while maintaining real-time processing at 30 FPS. The system significantly outperforms traditional approaches with 15.2% accuracy improvement over YOLO-only methods and 18.7% improvement over motion-only techniques. The proposed hybrid framework provides robust anomaly detection capabilities suitable for practical deployment in security-critical surveillance applications with reduced false positive rates and enhanced temporat consistence.