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Detector tree of boosted classifiers for real-time object detection and tracking

  • US 7,203,669 B2
  • Filed: 03/26/2003
  • Issued: 04/10/2007
  • Est. Priority Date: 03/17/2003
  • Status: Expired due to Fees
First Claim
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1. A method comprising:

  • building a tree classifier, which rejects non-object patterns in input data representing real world objects, including a plurality of parent nodes, wherein the tree classifier is stored on a machine-readable medium and is trained to perform human south detection and tracking in video sequences; and

    for a parent node in the tree classifier, selecting between a monolithic classifier as a child node and a plurality of specialized classifiers as child nodes for said parent node;

    wherein said selecting comprises;

    determining a computational complexity of a monolithic classifiers trained with a plurality of positive and negative samples; and

    determining a computational complexity of a plurality of specialized classifiers trained with the plurality of positive and negative samples, each of the specialized classifiers being trained with the plurality of negative samples and a different subset of the plurality of positive samples; and

    wherein human mouth detection and tracking in video sequences occurs when other tree classifier is executed.

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