Computer Vision Development

Turn visual data into usable evidence

Bounding boxes and prediction scores are only part of a working vision application. A team may also need a persistent object identity, a time window, calibrated distance, an event record and a way to inspect what the model saw.

My public work spans warehouse video analytics, satellite image classification and MRI research prototypes. VisionGuard AI, for example, combines detection, tracking, camera calibration and event evidence. The service can start with model development or extend an existing model into an application pipeline.

Inputs, outputs and scope

Inputs

Representative images or video, capture conditions, task definitions, labeling guidance, target hardware and examples of difficult or costly errors.

Outputs

A data and inference pipeline, trained or integrated model, evaluation report, visual outputs or event records and reproducible run instructions.

Decisions to agree

Class definitions, annotation quality, camera assumptions, acceptable false alarms and misses, and real-time or batch constraints.

Technical approach

1. Inspect the data and define errors

   Review capture variation, label consistency, class balance and the conditions the model will encounter. Split data to avoid overlapping subjects, scenes or sequences leaking between training and evaluation.

2. Build a task-specific baseline

   Choose detection, classification or segmentation according to the output required. For video, evaluate whether tracking and temporal rules are needed to turn frame predictions into meaningful events.

3. Add context and explanation

   In a safety video workflow, tracking history, zones and ground-plane calibration can add context to a detection. Save the relevant frame, clip or structured metadata so a reviewer can inspect the event rather than rely on a score alone.

4. Profile the complete pipeline

   Measure inference and end-to-end processing on the intended hardware and representative input sizes. Tune the model and data path around the actual latency and memory budget rather than a single isolated benchmark.

Delivery and evaluation

A first milestone establishes data quality and a reproducible baseline. Later milestones add the application output: an API, dashboard-ready events, stored predictions or an interactive review flow. Model files, preprocessing settings and label definitions need to travel together in the handover.

Evaluation follows the task. Detection needs precision, recall and localization checks; classification needs class-wise performance and a confusion matrix; tracking and events need temporal review and false-alarm analysis. Testing should include poor lighting, occlusion, camera changes and other relevant edge conditions. Reported results must identify the dataset and evaluation setup.

Relevant public work

These project pages provide concrete examples of related engineering and identify prototype or research scope where applicable.

Questions about this service

Can you work with existing CCTV footage?

Yes, if the footage is available for the intended use and represents the conditions to be analyzed. Camera position, resolution, occlusion and frame rate influence feasibility. Distance-based analysis may also require calibration.

Can the model run in real time?

That must be measured with the target hardware, input resolution and full pipeline. I would define the required processing rate and latency before selecting or optimizing the deployment approach.

What if we do not have labels yet?

We can begin by defining the task and reviewing a representative sample. An annotation guide and small reviewed dataset help establish whether model development is feasible and how much further labeling is needed.