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Robust Human Detection under Occlusion by Integrating Face and Person Detectors

  • William Robson Schwartz
  • Raghuraman Gopalan
  • Rama Chellappa
  • Larry S. Davis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Human detection under occlusion is a challenging problem in computer vision. We address this problem through a framework which integrates face detection and person detection. We first investigate how the response of a face detector is correlated with the response of a person detector. From these observations, we formulate hypotheses that capture the intuitive feedback between the responses of face and person detectors and use it to verify if the individual detectors’ outputs are true or false. We illustrate the performance of our integration framework on challenging images that have considerable amount of occlusion, and demonstrate its advantages over individual face and person detectors.

Keywords

False Alarm Partial Little Square Face Detector Detection Window Probability Interval 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • William Robson Schwartz
    • 1
  • Raghuraman Gopalan
    • 2
  • Rama Chellappa
    • 2
  • Larry S. Davis
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of MarylandUSA

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