arXiv:2606.12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language. We propose a three-stage pipeline that decomposes the accident understanding into when, what, and where. The first stage extracts a short temporal window around the impact using vision-language similarity. In the second stage, we perform metadata-driven multi-prompt reasoning with five complementary views (baseline, m
The continuous advancements in AI and computer vision, particularly in zero-shot learning and multi-modal reasoning, are enabling new applications in real-time video analysis.
This development represents a significant step towards autonomous, AI-driven surveillance and monitoring systems with direct implications for safety, security, and liability in various sectors.
AI systems can now understand complex events like accidents from surveillance video with greater nuance, identifying 'when, what, and where' without prior specific training data.
- · Surveillance technology providers
- · Insurance companies
- · Smart city initiatives
- · Law enforcement
- · Traditional manual surveillance monitoring
- · Companies with suboptimal safety protocols (due to increased detection)
Improved real-time accident detection and reporting from existing surveillance infrastructure.
Reduced response times for emergency services and more efficient accident investigation.
Enhanced automation of liability assessment in accident scenarios, potentially leading to new insurance models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI