Abstract:
The primary objective of this project is to leverage advanced technologies such as machine learning (ML), deep learning (DL), and large language models (LLMs) for real-time accident detection in traffic systems. By processing live traffic video streams, the system aims to analyze critical metrics such as vehicle count, speed, traffic density, and accident occurrence. A robust and efficient pipeline will be developed to handle the continuous influx of video data, enabling real-time accident detection with high accuracy. This system will also generate a comprehen- sive dataset from these video streams, aiding future studies on traffic management and accident prediction. The results of this analysis will be used to inform real-time responses, potentially improving traffic management systems and enhancing road safety. Keywords: Traffic Management, Accident Detection, Real-Time Processing, Multimodal Stream Analysis, Deep Learning, Large Language Models (LLMs), Vehicle Tracking, Contrastive Learn- ing, Video Segmentation, Traffic Density Analysis, Indian Accident Dataset, Data Annotation, Critical Frame Identification, Dataset Compilation, Classical Computer Vision, Probability- Based Accident Detection, Multimodal Vision-Language Models, Video Frame Sampling, Pipeline Optimization, Uncertainty Metrics.
