A Deep Spatio-Temporal Framework for Multi-Class Traffic Prediction and Accident Detection in Surveillance Video

Abstract

Traffic surveillance systems play a crucial role in intelligent transportation by enabling automated monitoring, traffic prediction, and accident detection. However, recognizing complex traffic scenarios from real-world videos remains challenging due to dynamic environments and temporal dependencies. This paper proposes a unified spatio-temporal framework that integrates YOLOv8 based object detection, convolutional neural networks for spatial feature ex traction, and long short-term memory networks for temporal modeling. Traffic videos are preprocessed to enhance visual consistency, and detected objects are transformed into structured spatial representations. Temporal dependencies across video sequences are learned using LSTM networks, and the extracted features are evaluated using multiple machine learning classifiers under different preprocessing strategies. Experimental results demonstrate that Z-score standardization improves classification performance, with Support Vector Ma chine achieving 63.27% accuracy and an F1-score of 55.66% in an eight-class traffic scenario classification task, indicating the feasibility and robustness of the proposed framework in real-world traffic environments.