Computer vision that turns camera feeds and images into counts, inspections and alerts

In most cases, yes. Any IP camera or NVR that provides an RTSP stream, or recorded footage we can export, can feed a computer vision system. Angle, resolution, lighting and frame rate for the specific event matter more than the camera brand. We review sample footage from each camera first and tell you which are suitable, which need repositioning and where an extra camera would help.

Motion detection reacts to any change in pixels, so it cannot tell a person from a swaying branch, a shadow or rain, which is why it produces so many alerts nobody needs. AI video analytics recognises what is in the frame, such as a person, vehicle or pallet, then applies rules about where it is, which way it moves and how long it stays. An alert can fire for a person in a closed yard after hours but not for a fox or headlights on the fence.

For a first test, a few hundred well-labelled examples per object type can be enough, because we fine-tune pretrained detection models rather than training from scratch. Those examples must cover day and night, busy and quiet periods and the variation you see in practice. A production model usually needs more, gathered from the cases the pilot gets wrong. Rare defects or unusual objects need deliberate collection, which we plan during discovery.

Run models on edge devices when a site has many cameras, weak connectivity or rules against video leaving the premises, and in the cloud when there are fewer cameras or recordings to analyse in batches. Edge processing sends only events onward, which saves bandwidth and keeps footage on site. Cloud processing suits heavier models and longer analysis of stored video. Many deployments use both, with detection at the edge and reporting, retraining and dashboards in the cloud.

Accuracy depends on how visible the defect is, how consistent the lighting is and the quality of labelled examples, so we measure it on your products rather than quote a general figure. During the pilot we report how many real defects were caught and how many good items were wrongly flagged, then set the threshold your quality team prefers. Controlled lighting at an inspection point often helps more than a larger model.

We combine a model trained on your own footage with rules that reflect how real incidents unfold. That includes detection zones, minimum durations, object size limits, schedules and confirmation across several frames before an alert fires. Operators can mark an alert as false, and those examples feed the next training round. We also agree an acceptable alert volume before launch, so the system stays useful rather than noisy.

It can be, when there is a lawful basis, a clear purpose, proportionate coverage and transparent signage. We design systems with UK GDPR in mind: counting and safety use cases can usually avoid identifying anyone, faces can be blurred, raw video retention can be short and access is logged. We are not lawyers, so we recommend a data protection impact assessment with your adviser and provide the technical detail it needs.

Yes. People counting, occupancy and queue measurement detect human shapes and follow anonymous movement between frames without recognising who anyone is. No face templates are created, and frames can be discarded as soon as counts are recorded. For most retail, venue and workplace analytics this anonymous approach provides the numbers needed while carrying far less privacy risk than identity-based systems.

You need cameras suited to the task, somewhere to run the models and a network link between them. Edge processing usually means a small GPU-equipped computer on site for each group of cameras, while cloud processing needs enough upload bandwidth for the streams being analysed. We specify hardware after the camera review, so you buy only what the chosen models require, and we can reuse equipment you already own.

A pilot on a handful of cameras for one well-defined event typically takes a few weeks after discovery and the footage review. The main drivers are the number of cameras and sites, the labelling effort and whether new hardware is needed. Extending to more sites is then mainly hardware installation, per-camera tuning and operator training, planned location by location so each one is proven before the next.

Cost depends on scope, and for vision work the biggest variables are labelling effort and on-site hardware rather than the model itself. The number of cameras and sites, how specialised the detection task is and where the models run also matter. After a discovery call and a review of sample footage, we send a written proposal with a fixed quote for the pilot and an estimate of hardware, hosting and support costs for a wider rollout, so you can judge value before committing to every site.

Accuracy can drop when packaging changes, new products appear or a camera is repositioned, so the system is built to adapt. Low-confidence detections and operator corrections are logged as candidate training examples. When something changes, we collect a short sample of new footage, retrain, compare the updated model with the current one on the same test set and roll it out through the existing deployment pipeline.

Yes. Each detection is stored as a structured event with time, camera, type, confidence and a snapshot or short clip. Events can send WhatsApp, email or Slack messages, open tickets in your service desk, update stock or production records in your ERP, or feed live dashboards. We build these links through APIs or n8n workflows, with routing rules that decide who receives which alert and when.

You do. Sampled frames, annotation files, the labelling guide, trained model weights, test results and the deployment configuration for each edge device are delivered into your repositories and cloud storage. Footage stays in the locations and for the retention periods agreed with you, and your images and labels are not reused to train models for anyone else. If you later bring the work in-house, your team can retrain and redeploy without rebuilding the dataset.