Multimodal stream processing for accident detection

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.