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Welcome to the IncidentGraph project Wiki. This documentation covers the evidence model, actor-centric activity recognition, cross-camera association, provenance-preserving graph reconstruction, contradiction handling, missing evidence, calibration, real-MEVA experiments, the controlled graph diagnostic, reproducibility, testing and responsible use.
Important
The current real-data study validates the local activity-evidence component. The complete graph pipeline is additionally tested with a controlled synthetic diagnostic. Do not present the synthetic graph score as real end-to-end multi-camera performance.
- Getting Started
- Architecture
- Evidence Model
- Activity Recognition
- Cross-Camera Association
- Graph Reconstruction
- Provenance and Uncertainty
- heterogeneous observation records for RGB, thermal, depth and other evidence sources;
- actor-centric local activity recognition;
- reliability-conditioned cross-camera association;
- typed entity and event graph construction;
- non-empty provenance on accepted event edges;
- explicit contradiction groups and alternative hypotheses;
- explicit missing-evidence objects;
- classifier calibration and uncertainty reporting;
- controlled system benchmarking under shared graph/evaluation interfaces;
- real-data and synthetic-diagnostic claim boundaries.
- Architecture
- Evidence Model
- Cross-Camera Association
- Graph Reconstruction
- Provenance and Uncertainty
Caution
Do not publish private camera footage, personal identifiers, access credentials, restricted dataset material, or outputs that could expose individuals.
IncidentGraph · evidence lineage preserved · conflicts retained · missing evidence remains unknown
IncidentGraph · provenance preserved · uncertainty explicit · missing evidence stays unknown · consequential use requires human review