Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

This paper presents a novel approach to anomaly detection in surveillance videos, focusing specifically on accident detection. Our proposed system integrates YOLOv8 and Convolutional Neural Networks (CNN) to create a hybrid model that efficiently detects accidents in real-time and generates alerts to the nearest police station. The YOLOv8 framework is employed for object detection, ensuring high accuracy and speed, while the CNN enhances the classification of detected anomalies. Additionally, we have implemented a vehicle license plate recognition system using YOLOv8 in conjunction with PaddleOCR for character detection, enabling the extraction of vehicle information during incidents. The results demonstrate the effectiveness of our approach in improving response times and enhancing public safety through automated alert generation and vehicle identification. This research contributes to the ongoing efforts in leveraging advanced machine learning techniques for real-world applications in surveillance and public safety.