Enhancing real-time human detection based on histograms of oriented gradients
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 a...
| Autores: | , , , |
|---|---|
| Formato: | capítulo de livro |
| Fecha de publicación: | 2007 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/2675 |
| Acesso em linha: | https://hdl.handle.net/2117/2675 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer vision Visió per ordinador Classificació INSPEC::Pattern recognition::Computer vision Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| Resumo: | 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. |
|---|