Solution · Business outcomes
Automated video analytics: every camera becomes a data source
Video is the most information-dense sensor most businesses own and the least measured. Automated analytics turns feeds into numbers — footfall, occupancy, dwell time, incidents — that land in an event store and API your tools can query.
What this gets you
Decisions from data, not from memory
Footfall by hour, occupancy trends, queue lengths at peak — measured continuously instead of guessed. The event store keeps timestamps, locations and confidence scores for every event.
Compliance and capacity, monitored automatically
Occupancy limits, restricted areas, safety corridors — enforced by detection and alerting instead of spot checks.
Alerts only for what matters
Deduplication and event-level logic mean overlapping cameras don't triple-count the same person, and your team isn't buried in noise.
Runs where your video lives
Edge devices (Jetson) for sites with limited bandwidth, or a central server where you have it — with ONNX/TensorRT optimization to squeeze the most out of the hardware.
How it works
1. Define the events that matter to the business
Before any model runs: what number or alert changes a decision? That defines the event schema — not the other way round.
2. Detection and tracking over your feeds
YOLO detection plus tracking, tuned for throughput on your hardware, produce consistent identities rather than raw detections.
3. Events land in a queryable store
Timestamps, camera IDs, bounding boxes and scores persist to MongoDB or PostgreSQL behind an API — consumable by your dashboards, not locked in the vendor's portal.
4. Dashboards and alerts on top
Whatever your team already uses — webhooks, WhatsApp alerts, BI tools pulling from the API. The data is yours.
Frequently asked questions
Is this the same as my NVR's motion detection?
No. Motion detection tells you pixels moved. Video analytics tells you what moved, where, when and for how long — as structured events you can query and count.
Can it count the same person across multiple cameras?
Tracking keeps identities consistent within a feed, and deduplication windows handle overlapping cameras. Re-identification across non-overlapping cameras is a harder problem and is scoped honestly if you need it.
Who owns the data?
You do. Events persist to your database behind your API — not a vendor portal you lose access to when the contract ends.
What's the deployment timeline?
A working pipeline on one feed typically comes first, then a measured throughput report, then scaling to your camera count. The exact shape gets agreed in a written scope before work starts.
Your question not here? Ask directly
Want this built for your business?
Book a free 15-minute call below — I'll tell you honestly whether this fits your situation, and what it would take.
How I work with clients