A Performance Evaluation of Single and Multi-feature People Detection

  • Christian Wojek
  • Bernt Schiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)


Over the years a number of powerful people detectors have been proposed. While it is standard to test complete detectors on publicly available datasets, it is often unclear how the different components (e.g. features and classifiers) of the respective detectors compare. Therefore, this paper contributes a systematic comparison of the most prominent and successful people detectors. Based on this evaluation we also propose a new detector that outperforms the state-of-art on the INRIA person dataset by combining multiple features.


Haar Wavelet Equal Error Rate False Positive Detection Pedestrian Detection Wavelet Feature 
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 2008

Authors and Affiliations

  • Christian Wojek
    • 1
  • Bernt Schiele
    • 1
  1. 1.Computer Science DepartmentTU Darmstadt 

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