Many cameras.
One space.
We align anonymized signals into one coordinate system and journey.
Signal
Anonymized signals
Coordinate
Aligned to one coordinate system
Journey
One journey
MTMC merges observations from many cameras into one spatial coordinate system.
Journey Proof
A journey is an event record, not a single frame
Connect observations scattered across cameras on the same coordinates, and everything from entry to exit stays as one record.
One journey on shared coordinates
- Customer
- Staff
- Trajectory
Event 1 / 4 · 10:02 Entrance crossed
Anonymous event log
Select an event to highlight that point on the map.
Zone classification derived from the same observation
The baselines are the medians across all zones, drawn on the chart. Each quadrant follows from the visit and dwell coordinates of the zone.
Illustrative simulation in a virtual space. Values do not represent actual performance.
Zone quadrant matrix
Visits and average dwell per visit sort every zone into four types. The baselines are the medians across all zones.
- ALow visits · high dwellvisits 96 · dwell 10.1s
- BHigh visits · low dwellvisits 573 · dwell 1.4s
- CLow visits · high dwellvisits 96 · dwell 2.5s
- DHigh visits · high dwellvisits 430 · dwell 5.1s
- EHigh visits · low dwellvisits 478 · dwell 1.3s
AI contrast
Same question, different answer
An AI that cannot see the space stays with generalities. With coordinates and journeys, SAAI answers with the numbers of this store.
Same Question
"Yesterday evening, which product drew the longest dwell?"
Ask a generic AI
Ask SAAI
* Both screens show simulated demo data for a virtual store.
Spatial capabilities
Read signals in the language of space
The four capabilities behind that difference.
Signals from one feed
- People, pose, and density
- Gaze and fixture contact
Anonymize · recognize · space · flow · change — a digest of the 5-category model catalog.
Cameras into one coordinate system
- One flow across cameras
- Journeys, dwell, and density on one map
A scene as a question
- Describe what is happening
- Read context beyond coordinates
Spatial signals for operations
- Interpret signals by space
- Connect them to the next decision
Why This Is Possible
Anonymization comes first
Identity disappears on input. Only the signals needed to read a space continue downstream.
More on the anonymization technologyFaces erased, context preserved
Identity is permanently erased, retaining only minimum features needed for scene understanding.
Faces masked as noise. Still readable — age, gaze, speech. Zero identity.
References
DEEPINGSOURCE, refining video anonymization technology since 2018, has presented and validated spatial intelligence at CES, in collaboration with KDDI, and within the NVIDIA ecosystem. Relevant presentation and collaboration records are available as reference materials.
Set the standard for reading your space
Review camera placement and analysis goals with our technical team.