AI video analytics architecture
Camzify AI architecture consists of six processing layers: Object Detection, Multi-Object Tracking, Custom Domain Models (industry-specific classes), Vision-Language Model (attributes and natural-language context), Signal Analysis (tampering, motion gating, stream health, no GPU required), and Adaptive Inference (skips static frames, full attention on active scenes).
Locates people, vehicles, and other objects of interest in each frame.
Follows each detected object across frames to build a continuous path, not isolated snapshots.
Industry-specific object classes trained for the environment being monitored, on top of general detection.
Adds attributes and natural-language context to detections rather than just a bounding box and a label.
Handles tampering, motion gating, and stream health — this layer needs no GPU at all.
Skips static frames and puts full attention on active scenes, so compute goes where the activity is.
The six processing layers
GPU-efficient signal analysis
Tampering, motion gating, and stream health run on signal-processing logic rather than a neural network, so this layer needs no GPU at all.
Adaptive inference saves compute
Static frames get skipped rather than reprocessed, so full model attention, and the compute budget, goes to scenes that are actually active.
Custom models per industry
Domain models carry object classes trained for the vertical being monitored, not a generic one-size-fits-all detector.
Natural-language context
The vision-language layer adds descriptive attributes on top of detections, so alerts read as description, not just coordinates.
Better accuracy, tuned independently
A single monolithic model has to be everything at once: detector, tracker, domain classifier, and language layer rolled into one set of weights. Splitting those responsibilities across six purpose-built layers means each one can be evaluated, tuned, or retrained on its own, without the risk of a fix in one area quietly degrading another.
It also concentrates compute where it's actually needed. Signal Analysis runs on CPU because it doesn't need a neural network to spot a tampered lens or a dead stream, and Adaptive Inference skips frames where nothing changed rather than re-running the full pipeline on every single frame regardless of activity. This module integrates with virtual patrolling, so detections from every layer feed the same operational picture.
Frequently asked questions
Ready to patrol your site 24/7?
Book a 15-minute demo and see a live patrol run on your own cameras.