TR2004-043

Rapid Object Detection Using a Boosted Cascade of Simple Features


This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the Integral Image which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a cascade which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

 

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    •  AWARD   CVPR 2011 Longuet-Higgins Prize
      Date: June 25, 2011
      Awarded to: Paul A. Viola and Michael J. Jones
      Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
      Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contact: Michael Jones
      Research Area: Machine Learning
      Brief
      • Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).
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    •  NEWS   CVPR 2001: 4 publications by Paul Beardsley, Matthew Brand, Ramesh Raskar and Michael Jones
      Date: December 9, 2001
      Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contacts: Michael Jones; Matthew Brand
      Brief
      • The papers "Morphable 3D Models from Video" by Brand, M.E., "Flexible Flow for 3D Nonrigid Tracking and Shape Recovery" by Brand, M.E. and Bhotika, R., "A Self-Correcting Projector" by Raskar, R. and Beardsley, P.A. and "Rapid Object Detection Using a Boosted Cascade of Simple Features" by Viola, P. and Jones, M. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
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