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 bject 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 Gould
• Joakim Arfvidsson
• Adrian Kaehler
• Benjamin Sapp Ashutosh Saxena
• Marius Messner
• Gary Bradski
• Paul Baumstarck
• Sukwon Chung
• Andrew Y. Ng

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