Anomaly Detection in Video Surveillance Using YOLOv8

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Hybrid approach improves accuracy in video surveillance anomaly detection, highlighting YOLO integration with motion analysis.

Improved anomaly detection accuracy of 92.3% was achieved through a novel hybrid method.
Experimental evaluation shows a 15.2% accuracy increase over YOLO-only methods and 18.7% over motion techniques.
The proposed system utilizes YOLOv8 and optical flow in a parallel processing framework for real-time analysis.
This hybrid architecture enhances robustness in security applications, reducing false positives significantly.

Praveen et al. (2025) studied this question.

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