Year: 2007
Type: conference-paper
Abstract: Human object recognition in a physical 3-d environment is still far superior to that of any robotic vision system.We believe that one reason (out of many) for this-one that has not heretofore been significantly exploited in the artificial vision literature-is that humans use a fovea to fixate on, or near an object, thus obtaining a very high resolution image of the object and rendering it easy to recognize.In this paper, we present a novel method for identifying and tracking objects in multi-resolution digital video of partially cluttered environments.Our method is motivated by biological vision systems and uses a learned "attentive" interest map on a low resolution data stream to direct a high resolution "fovea."Objects that are recognized in the fovea can then be tracked using peripheral vision.Because object recognition is run only on a small foveal image, our system achieves performance in real-time object recognition and tracking that is well beyond simpler systems.
Authors: Stephen Jay Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary Bradski, Paul Baumstarck, Sukwon Chung, Andrew Y. Ng
Institution: Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G440206934, G4713059963
OpenAlex ID: W79820356