Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding

Researchers present a three-stage pipeline for zero-shot accident detection in surveillance videos that combines temporal localization, semantic classification, and spatial grounding using vision-language models. The method decomposes accident understanding into when, what, and where components, achieving significant improvements over baseline approaches on the ACCIDENT benchmark.

This research addresses a critical gap in computer vision by enabling machines to understand accident events from surveillance footage without task-specific training data. The proposed methodology moves beyond direct prompting of vision-language models by introducing a structured decomposition approach that treats temporal detection, semantic reasoning, and spatial localization as distinct subtasks. This separation mirrors how human analysts process security footage, suggesting that structured reasoning outperforms monolithic approaches.

The innovation lies in the metadata-driven multi-prompt reasoning framework, which leverages five complementary analytical perspectives to reduce hallucinations and improve reliability. The entropy-gated adjudicator mechanism for resolving disagreements between different reasoning paths represents a practical solution to a fundamental problem in large model deployment: managing conflicting outputs without human intervention.

For the surveillance and security industry, this approach has immediate implications. Accident detection systems currently require extensive labeled data and domain-specific tuning, making deployment expensive and time-consuming. A zero-shot capability substantially reduces these barriers, enabling rapid deployment across diverse environments and accident types. This could democratize advanced video analytics for smaller organizations lacking resources for data annotation and model fine-tuning.

The broader significance extends to foundation model evaluation and prompting strategies. The work demonstrates that decomposing complex visual reasoning tasks into structured pipelines yields better results than treating them monolithically, providing a template for other video understanding challenges. As vision-language models become increasingly central to enterprise applications, understanding how to architect reasoning workflows around their strengths becomes a competitive advantage.

• →Three-stage pipeline decomposing accident understanding into temporal, semantic, and spatial components achieves substantial improvements over direct prompting baselines.
• →Multi-prompt reasoning with entropy-based disagreement resolution reduces hallucinations and improves reliability in zero-shot video understanding tasks.
• →Zero-shot accident detection capability could dramatically reduce deployment costs and timelines for surveillance systems across diverse environments.
• →Structured decomposition of complex visual reasoning outperforms monolithic approaches to vision-language model prompting.
• →The methodology provides a reusable template for architecting other complex video understanding tasks around foundation model strengths.