Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video
Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary Bradski, Paul Baumstarck, Sukwon Chung, Andrew Y. Ng
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.