---
title: "Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video"
authors:
  - "Stephen Gould"
  - "Joakim Arfvidsson"
  - "Adrian Kaehler"
  - "Benjamin Sapp"
  - "Marius Messner"
  - "Gary R. Bradski"
  - "Paul Baumstarck"
  - "Sukwon Chung"
  - "Andrew Y. Ng"
doi: ""
url: "https://ijcai.org/proceedings/2007/341"
pdf: "https://ijcai.org/proceedings/2007/0341.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2115-2120"
track: ""
keywords:
---

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

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

*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.