Not benchmark demos — deployed systems: a surveillance AI watching 8+ simultaneous camera feeds at 91% detection accuracy, and a wearable-sensor pipeline classifying human movement at 92%+. You can watch the live demo videos on this site.
Manufacturers and facility operators who want visual inspection or monitoring without a research team
What you get
Object detection & tracking
YOLOv8-class detectors with multi-object tracking, tuned to your footage — not stock weights. My CCTV system cut false positives by 35% with per-zone threshold tuning.
Real-time video analytics
Multi-camera ingestion (RTSP/FFmpeg), on-stream inference, and WebSocket alert delivery to operator dashboards — 8+ simultaneous feeds in production.
OCR & document intelligence
Text extraction from scans, photos, and industrial displays using Tesseract, OpenCV, and deep learning — a service line I’ve delivered for international clients since 2020.
Pose & motion analysis
Human movement understanding from video and sensors — gait analysis and kinematics at 92%+ movement-classification accuracy in a clinical rehab platform.
Edge & cloud deployment
ONNX-optimised inference (2–3× faster on CPU), Dockerised serving, and the edge-vs-cloud tradeoff analysis for your latency and bandwidth budget.
CCTV Anomaly AI — real-time surveillance intelligence
Problem
A security operation needed automatic detection of crowd surges, loitering, intrusion, and fights across many cameras — without an operator glued to every screen, and without alert fatigue.
Built
YOLOv8 detection + multi-object tracking with a custom anomaly engine (per-zone dwell, red-zone entry, crowd density), snapshot evidence per alert, and WebSocket push to an operator dashboard. Evolved over four phases from prototype to production MVP.
On well-scoped problems with decent footage: 85–95% out of the gate, improving with tuning on your data. My production surveillance system runs at 91%. Anyone promising 99% before seeing your cameras is selling, not engineering — accuracy is measured on your footage, in your lighting, at your angles.
How much training data do we need?
Often less than you think. Modern detectors fine-tune well on hundreds — not millions — of labelled examples, and for common object classes we start from strong pretrained weights. The audit includes a data assessment before you commit to anything.
Edge or cloud?
Edge when bandwidth, privacy, or sub-100ms latency demands it; cloud when you need fleet-wide models and easy updates. I’ve shipped both — ONNX-quantised models on constrained hardware and multi-stream GPU inference in the cloud — and will model the cost of each for your camera count.
Can you work with our existing CCTV/IP cameras?
Almost certainly. If it speaks RTSP (nearly every IP camera does), the ingestion layer I use — FFmpeg-based decoding into a frame buffer — handles it without hardware changes.
Have a project in mind?
A free 30-minute call — you describe the problem, I tell you honestly whether and how I'd solve it.