Use cases · Perception · Real-time video
The camera that names
what it sees.
Raw city footage in; named, tracked objects out — live, on a laptop CPU, with no pretrained model and no GPU. Senua AI learns the structure of the scene from the video stream itself, assigns its own object identifiers, and names what it has been shown names for — with a confidence it reports and an abstention it is not ashamed of.
What you are watching
No detector. No training set. The stream is the teacher.
Every deployed vision system today is a pretrained detector: a GPU-scale network, trained offline on millions of labelled images, that recognises only the classes frozen into it at training time. Senua AI inverts the pipeline. One of its most powerful capabilities is identifying causation from the moments a stream provides — cognition follows. Pointed at video, every region of every frame is a stream of moments, and what persists, moves, and recurs in those moments is the scene’s structure.
Object identity is not looked up in a trained catalogue; it is discovered as persistence in the learned states, and the engine assigns its own identifiers before any human name enters the picture. Names arrive afterwards, as grounding: show it a handful of labelled moments — this is sky, this is water, this is the Superdome — and it carries those names forward on its own evidence. Teaching it a new object class is a data operation performed in the field, not a retraining cycle in someone’s cloud.
How Senua AI handles it, end to end
One run, three things happening at once — live, on commodity hardware.
1 · Learn the scene
The engine watches the raw feed and learns the states of the scene from the stream itself — day and night, across hard cuts — while training in real time through self-learning and annealing. No pretrained weights, no labels required to start.
2 · Track what persists
What holds together across frames becomes an object with a machine-assigned identifier — before it has a human name. Skyline towers, the river, vessels at the pier: tracked as persistent identities, boxed live on the video.
3 · Name with evidence
A handful of grounded moments teach names — sky, building, water, boat, the lit cathedral, the Superdome. Each label carries its confidence, and where evidence is missing the engine shows its internal tag instead of guessing. No hallucinated classes, ever.
Honesty, by construction
It reports only what it can back.
This is a testing-harness run of capabilities in active development, recorded as-is. The lit night vessels persist and name; a distant barge registers only transiently at the current spatial granularity, and the bridge’s local dynamics are indistinguishable from generic buildings at this resolution — so both stay tracked-but-unnamed rather than guessed. That restraint is the design: in a forced-choice classifier the failure mode is a confident wrong answer; here the failure mode is an honest “not yet.” Finer spatial granularity and public vision benchmarks are the next, pre-registered steps.
Point it at your footage.
Send us a clip; get it back identified — then teach it your own object classes, in your own environment, on your own hardware. No GPU, no cloud, no model to license.