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PittPatt Face Detection and Tracking for the CLEAR 2006 Evaluation

  • Michael C. Nechyba
  • Henry Schneiderman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

This paper describes Pittsburgh Pattern Recognition’s participation in the face detection and tracking tasks for the CLEAR 2006 evaluation. We first give a system overview, briefly explaining the three main stages of processing: (1) frame-based face detection; (2) motion-based tracking; and (3) track filtering. Second, we summarize and analyze our system’s performance on two test data sets: (1) the CHIL Interactive Seminar corpus, and (2) the VACE Multi-site Conference Meeting corpus. We note that our system is identically configured for all experiments, and, as such, makes use of no site-specific or domain-specific information; only video continuity is assumed. Finally, we offer some concluding thoughts on future evaluations.

Keywords

False Alarm Ground Truth False Alarm Rate Face Detection Visible Landmark 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Schneiderman, H.: Feature-centric evaluation for efficient cascaded object detection. CVPR 2, 29–36 (2004)Google Scholar
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    Schneiderman, H., Wavering, A.J., Nashman, M., Lumia, R.: Real-Time Model-Based Visual Tracking. In: Proc. Intelligent Robotic Systems ’94 (July 1994)Google Scholar
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    Bierman, G.: Factorization Methods for Discrete Sequential Estimation. Academic Press, New York (1977)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Michael C. Nechyba
    • 1
  • Henry Schneiderman
    • 1
  1. 1.Pittsburgh Pattern Recognition, 40 24th Street, Suite 240, Pittsburgh, PA 15222USA

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