Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

  • Authors:

  • Qiang Zhu;Mei-Chen Yeh;Kwang-Ting Cheng;Shai Avidan

  • Affiliations:

  • University of California at Santa Barbara, CA;University of California at Santa Barbara, CA;University of California at Santa Barbara, CA;Mitsubishi Electric Research Laboratories 201 Broadway, Cambridge, MA

  • Venue:
  • CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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 脳 280 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.