Computer Vision
Your warehouse camera sees everything. Your software sees nothing.
Most camera AI only works in perfect lighting. Real businesses deal with shadows, blocked views, moving water, and objects that do not sit still. I build systems that work where the work happens — extracting 491 measurements from a person, counting tiny oyster seed, or inspecting gear fast enough to keep up with the team.
What Usually Looks Normal Until I Point At It
A person counting or inspecting things by hand, all day, with a known margin of error nobody has fixed
Off-the-shelf vision tools that work in the demo and fail in your warehouse
Latency too high for real-time use: models that run in seconds, not milliseconds
Privacy concerns send sensitive images to third-party APIs
How I Build It
Start with the specific detection, measurement, or identity problem
Select the vision approach by accuracy, latency, and the kind of scene the business actually has
Build pipelines that handle real-world variation and occlusion
Deploy on owned hardware or edge devices based on latency and privacy needs
Outcomes
491-field face/body inventory for style, wellness, or identity applications
Instant subject isolation in video
Technology Stack
Capabilities
Face & Body Analysis
A single system reads pose, outline, and depth at once. It measures the face and creates a unique body profile—checking face shape, symmetry, body type, posture, stance, and specific measurements.
How It Works
- Unified backbone extracts pose, segmentation, depth, normal, pointmap
- Face analyzers measure shape, symmetry, thirds, subregions
Outcomes
- 491-field face/body inventory per subject
- Body-twin matching via 45-dimensional shape fingerprint
Detection, Segmentation & Tracking
I combine segmentation, detection, measurement, and tracking — calibrated for your camera, lighting, and throughput.
How It Works
- Select the detection path by task: masks, counts, measurements, or tracking
- Calibrate pixel-to-world measurements for the deployment camera
Outcomes
- Real-time counts in variable field conditions
- Pixel-accurate segmentation for measurement and masking
Related Case Studies
The research is public.
Benchmarks run on machines I own, measured against frontier systems — with the failed approaches reported next to the results. Read it before you take my word for anything.