Make sensor data visible in context.
Explore how camera geometry and sensor coordinates come together to explain movement, alignment, and the physical world.
Computer Vision & Sensor Visualization demonstration
Independent visualization
Sensor projection workspace
A synthetic trajectory and schematic camera view illustrate the projection idea. This independent graphic does not contain real radar recordings or employer footage, and it is not a measurement-accuracy demonstration.
01 / The problem
What needs to work better.
A sensor can measure a position in three dimensions while a camera records pixels. Connecting those views requires an explicit relationship between the sensor, the camera lens, and the physical scene.
02 / Engineering approach
Build a useful system around it.
Calibrate the lens to understand focal length, image center, and distortion for a specific camera configuration.
Establish the camera’s position and orientation relative to the sensor using measured reference geometry.
Transform sensor points into the camera coordinate frame, project them into the image, and account for display scaling and timing.
Inspect the projection with reference points and a 3D workspace, making alignment errors visible before interpreting the result.
03 / Demonstrated capability
What this work shows.
Working across camera calibration, coordinate systems, and interactive visualization. The underlying radar-to-video project remains an evolving prototype: reliable camera-to-sensor alignment and integration of calibration into the main overlay are ongoing engineering concerns.
04 / Business applications
Where the same thinking can help.
- Technical visualization and training tools
- Sensor-data review interfaces
- Camera and equipment alignment workflows
- Feasibility prototypes for spatial applications
These are potential applications, not a list of past Studio client engagements.
For the technically curious
- Relevant experience includes Python, OpenCV, ChArUco calibration, 3D-to-2D projection, and interactive camera workspaces.
- Lens calibration alone does not locate the camera relative to the sensor. Both position and orientation are needed.
- A production application would require measured error tolerances, repeatable validation, synchronization, and testing across the supported camera configurations.
Further reading
Explore the engineering background.
Explore the professional projects behind this concept. Each case study explains my contribution, the tools involved, and the current project status.
Radar-to-video calibration projectStart with your operation
What would make your work easier?
A messy workflow, a disconnected system, or an idea worth testing. That’s a useful place to start.