CHAMPAIGN, Ill. By recognizing both visual and audio cues, a self-aiming camera being developed at the University of Illinois can tell the difference between an airplane and an albatross.
The camera system, which could find use as an intelligent sentinel in sensitive military applications, originally was built to demonstrate the versatility of a simulated neural network, which the researchers modeled after the superior colliculus of the human brain.
The superior colliculus serves as the visual reflex center of the brain, said Sylvian Ray, a UI professor of computer science and a researcher at the Beckman Institute for Advanced Science and Technology. It is the primary agent for deciding which direction to turn the head in response to sensory stimuli such as visual and auditory cues.
To demonstrate the effectiveness of their neural network, Ray and his colleagues molecular and integrative physiology professor Thomas Anastasio, postdoctoral research associate Paul Patton, and graduate research assistants Samarth Swarup and Alejandro Sarmiento constructed a camera and microphone system that supplies visual and auditory cues to the model and responds to its directives.
One camera looks for motion by comparing successive video frames while the system monitors audio signals from a pair of omnidirectional microphones. A sound-location algorithm analyzes the sounds and sends the information to the neural network. The model then determines the correct position and moves a second camera, equipped with a long-focus lens, to acquire the target. This target image can be transmitted to a human operator for further analysis.
While the system can be attracted by either sight or sound, the combination of the two offers a much stronger stimulus, Ray said. By using look-up libraries of sight and sound, the system can differentiate between an aircraft on the horizon and a flock of birds.
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Contact: James E. Kloeppel
University of Illinois at Urbana-Champaign