VESSI - VLM-Enhanced Support for Surveillance and Investigations
To address this, we propose VLM-Enhanced Support for Surveillance and Investigations (VESSI), a VLM-based framework designed to enhance automated video surveillance analysis through prompt-driven interrogation of video sequences where salient visual features are converted into textual descriptions.
ProofPaper ↗
Key points
- Automated video surveillance analysis has become a critical component of intelligence infrastructures and Law Enforcement agencies.
- Traditional systems lack the semantic module for comprehensive situational awareness and forensic tasks, limiting their ability to interpret events meaningfully or support post-incident investigations.
- Recent advances in Vision-Language Models (VLMs) offer promising pathways to bridge this gap.
- Since most datasets for this task are unlabeled, we also propose the Composite Model Utility Score (CMUS) to assess VLM performance.
Sources (1)
- [1]VESSI - VLM-Enhanced Support for Surveillance and InvestigationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:50 AM
To address this, we propose VLM-Enhanced Support for Surveillance and Investigations (VESSI), a VLM-based framework designed to enhance automated video surveillance analysis through prompt-driven interrogation of video sequences where salient visual features are converted into textual descriptions.
Automated video surveillance analysis has become a critical component of intelligence infrastructures and Law Enforcement agencies.
Extractive summary: sentences quoted from the sources.