Peripheral-foveal vision for real-time object recognition and tracking in video

  • Authors:

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

  • Affiliations:

  • Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA

  • Venue:
  • IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
  • Year:
  • 2007

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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 multiresolution 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.