Researchers have developed an intelligent camera that achieves not only high spatial and temporal but also spectral resolution. The camera has a wide range of applications that can improve environmental protection and resource conservation measures as well as autonomous driving or modern agriculture.
Research previously has focused on increasing spatial and temporal resolution, which means the number of megapixels or images per second. Spectral resolution — the wavelength and thus the perception of colors — has largely been adjusted to match human sight during the development of cameras, which merely corresponds to measuring the colors of red, green, and blue. There is much more information hidden in the light spectrum that can be used for a wide range of tasks.
The high-resolution camera combines all three resolutions — spatial, temporal, and spectral — in a cost-efficient solution. The researchers connected several inexpensive standard cameras with various spectral filters to form a multi-spectral camera array. They then calculated an image in order to combine the various spectral information from each sensor. The new concept enables the team to precisely determine the materials of each object captured using just a single image.
As the surroundings are recorded by several “eyes” the way humans see, the system also provides a precise indication of depth. This means that the system not only precisely determines the color and certain material properties of objects it captures but also the distance between them and the camera.
Autonomous driving is a potential application for the intelligent camera. In the infrared range, for example, it can differentiate between real people and signposts using the thermal signature. For night driving, it can detect animals crossing the road with sufficient warning. The camera could also be used for protecting the environment and conserving resources; for example, several plastics differ significantly from each other in various ranges of the spectrum, which is something the camera can reliably detect.
For more information, contact PD Dr. Jürgen Seiler, Multimedia Communications and Signal Processing Department, at juergen.