Boosting histograms of oriented gradients for human detection

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature selection and Support Vector Machine as weak classifier, we build up a fast human classifier with an excellent detection rate.

This work is supported by EC grants IST-027110 for the HERMES project and IST-045547 for the VIDI-video project, and by the Spanish MEC under projects TIN2006-14606 and DPI-2004-5414. Jordi Gonzàlez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC.

This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).

Presentado al 2nd Computer Vision: Advances in Research & Development celebrado en 2007 en Bellaterra (Spain).

Peer Reviewed

Country

Spain

Related Organizations

Universitat Polite`cnica de Catalunya
Spain
Spanish National Research Council
Spain

Keywords

Classificació INSPEC::Pattern recognition::Computer vision, Pattern recognition, pattern recognition, Visió per ordinador, Computer vision, Object recognition, machine vision, Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo, :Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo [Àrees temàtiques de la UPC], Machine vision, :Pattern recognition::Computer vision [Classificació INSPEC], object recognition

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average