Enhancing real-time human detection based on histograms of oriented gradients

Marco Pedersoli, Jordi Gonzàlez, Bhaskar Chakraborty, Juan J. Villanueva

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


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 real-time human classifier with an excellent detection rate. © 2007 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Pages (from-to)739-746
JournalAdvances in Soft Computing
Publication statusPublished - 1 Dec 2007


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