The problem
Accurately estimating the distance of a vessel from a camera is a fundamental challenge in maritime surveillance. Conventional approaches often rely on calibrated cameras, known camera parameters, stereo setups, or controlled environments — making them difficult to deploy across different platforms and camera systems.
This raises an important question: can we estimate a ship's distance in real time, regardless of the camera being used, while simultaneously identifying the vessel?
Our approach
We have developed a real-time, camera-agnostic vision system that estimates the distance of vessels and performs ship classification directly from video feeds, without requiring the system to be tied to a specific camera model or fixed imaging configuration.
Our algorithms leverage visual cues and learned vessel characteristics to estimate range in real time, while simultaneously detecting and classifying ships within the scene.
One camera. Real-time intelligence.
The solution enables:
- Camera-agnostic distance estimation across different cameras and imaging platforms.
- Real-time vessel detection and tracking from live video.
- Automated ship classification based on visual characteristics.
- Simultaneous distance estimation and classification, eliminating the need for separate processing pipelines.
- Adaptability across diverse maritime environments and camera configurations.
The result is a flexible vision system that transforms a conventional camera feed into real-time maritime situational awareness — providing not only what the vessel is, but also how far away it is.