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.
CitationPerdersoli, Marco; Gonzàlez, Jordi; Chakraborty, Bhaskar; Villanueva, Juan J.. "Enhancing real-time human detection based on histograms of oriented gradients". 5th International Conference on Computer Recognition Systems (CORES), Wroclaw, Polònia , 2007. A: Advances in Soft Computing, vol. 45. : Springer, 2007, p. 739-746.
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