Speeding up HOG and LBP features for pedestrian detection by multiresolution techniques

Philip Geismann, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In this article, we present a fast pedestrian detection system for driving assistance. We use current state-of-the-art HOG and LBP features and combine them into a set of powerful classifiers. We propose an encoding scheme that enables LBP to be used efficiently with the integral image approach. This way, HOG and LBP block features can be computed in constant time, regardless of block position or scale. To further speed up the detection process, a coarse-to-fine scanning strategy based on input resolution is employed. The original camera resolution is consecutively downsampled and fed to different stage classifiers. Early stages in low resolutions reject most of the negative candidate regions, while few samples are passed through all stages and are evaluated by more complex features. Results presented on the INRIA set show competetive accuracy performance, while both processing and training time of our system outperforms current state-of-the-art work.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 6th International Symposium, ISVC 2010, Proceedings
Pages243-252
Number of pages10
EditionPART 1
DOIs
StatePublished - 2010
Event6th International, Symposium on Visual Computing, ISVC 2010 - Las Vegas, NV, United States
Duration: 29 Nov 20101 Dec 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6453 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International, Symposium on Visual Computing, ISVC 2010
Country/TerritoryUnited States
CityLas Vegas, NV
Period29/11/101/12/10

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