Proceedings

Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video

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Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2115-2120.

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 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.

Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary R. Bradski, Paul Baumstarck, Sukwon Chung, and Andrew Y. Ng. 2007. Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 2115–2120. https://ijcai.org/proceedings/2007/341
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