Fast human detection using a cascade of histograms of oriented gradients

Qiang Zhu, Shai Avidan, Mei Chen Yeh, Kwang Ting Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1114 Citations (Scopus)

Abstract

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 250 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages1491-1498
Number of pages8
DOIs
Publication statusPublished - 2006 Dec 22
Externally publishedYes
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: 2006 Jun 172006 Jun 22

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
CountryUnited States
CityNew York, NY
Period06/6/1706/6/22

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Zhu, Q., Avidan, S., Yeh, M. C., & Cheng, K. T. (2006). Fast human detection using a cascade of histograms of oriented gradients. In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 (pp. 1491-1498). [1640933] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2). https://doi.org/10.1109/CVPR.2006.119