ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search
Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and time. We introduce ARGOS (Agentic Retrieval with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a complete query into interactive reasoning from partial clues. To our knowledge, ARGOS is the first interactive benchmark to couple witness dialogue with camera-network topology, requiring an agent to plan, question, and eliminate under information asymmetry. An ARGOS agent receives a vague witness statement and must decide what to ask,...
| Authors | Myungchul Kim, Kwanyong Park, Junmo Kim et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 April 2026 |
| Last updated at source | 4 September 2026 |
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