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Observe real conditions
We sample video and images to understand angles, lighting, motion, occlusion, resolution, and variation between environments.
For companies that need to detect, follow, read, or measure what happens in images and video.
Computer vision development for real images, video, and streams
We design the complete system: data collection and annotation, model, tracking, application logic, deployment, and the interface used by operators.
Assess the video streamsThe vision system beyond the model
- Object detection, classification, segmentation, and tracking
- OCR and event search across recorded footage
- Real-time video analytics on existing streams
- Local or edge inference when latency, bandwidth, or data control requires it
Components we can deliver
- An audit of cameras, streams, datasets, and operating conditions
- An annotation protocol, benchmark, and scenario-specific metrics
- A reproducible training, validation, and inference pipeline
- An API, event index, dashboard, or on-device integration
From raw footage to an operational workflow
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Measure the model and pipeline
We define the dataset and metrics, then evaluate accuracy, false positives, latency, and throughput on the intended hardware.
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Integrate the result
We connect inference, rules, storage, and interface, including human review and traceability where required.
Computer vision inside complete products
AiLights applies tracking to real-time sports video production. Targage indexes existing surveillance streams and makes licence plates searchable over time.
Questions about computer vision development
Do we need an existing dataset?
Not necessarily. We can start with representative samples and define collection, annotation, splits, and validation criteria.
Do you work with real-time video?
Yes. We assess latency and throughput using the actual stream, resolution, and hardware available.
Can you use cameras that are already installed?
Often yes, but compatibility, stream quality, and access through protocols such as RTSP or ONVIF must be checked for the specific setup.
How do you handle false positives and errors?
We measure them by scenario and design thresholds, rules, human review, and logging around the operational cost of an error.
Do you have representative footage or streams?
Describe what must be detected, under which conditions, and how often. A short sample usually reveals where the main technical risk sits.
Discuss the vision system