Multimodal AI Emergency Event Detection System: Integration of Computer Vision and Vision-Language Models

The Multi-Agent Emergency Detection System is a multimodal AI system integrating computer vision and vision-language models. It can detect emergency events in real time and assess their severity, and is equipped with an interactive Streamlit dashboard supporting video stream analysis, frame extraction, intelligent description generation, and event classification. The project is maintained by shivanggupta23, sourced from GitHub (link: https://github.com/shivanggupta23/Multi-Agent-Emergency-Detection-System_Using_OpenCV), updated on 2026-06-05.

Traditional monitoring relies on manual observation, which has problems like response delays and attention distraction; single-modal detection has limitations (pure vision lacks semantics, pure text lacks spatial information), so multimodal fusion is a key direction to improve detection accuracy.

The system adopts a multi-agent collaboration architecture (video analysis, frame extraction, visual understanding, classification decision agents); builds a visual processing pipeline based on OpenCV; integrates vision-language models to convert images into natural language descriptions; provides an interactive interface via the Streamlit framework (video upload, real-time stream access, result visualization, etc.).

Supports multiple video input sources (local, RTSP, network videos); intelligent frame extraction (motion analysis + scene change detection to retain key frames); scene description generation (vision-language models output semantic descriptions of events); event classification (fire, traffic accidents, etc.) and severity assessment (ranked based on number of people involved, danger level, etc.).

Public safety monitoring (assists in locating abnormal events), industrial safety management (monitors hidden dangers and issues warnings), traffic management (detects accidents/congestion), emergency response training (provides materials).

Multimodal fusion strategy (CNN visual features + VLM text descriptions to improve classification performance); real-time performance optimization (model quantization, batch processing, asynchronous pipeline); scalable architecture (multi-agent design makes it easy to add new detection capabilities).

Current limitations: In production environments, large-scale concurrency, edge deployment optimization, fine-grained event coverage, and system integration interfaces need to be considered; future directions: temporal modeling to improve dynamic event recognition, integration of audio multimodal analysis, and development of mobile applications.

This system achieves the leap from "seeing" to "understanding", providing intelligent auxiliary decision-making capabilities for emergency response; it provides a complete reference implementation (data processing → model integration → interface development) for developers in the AI security monitoring field.