Fast human detection based on enhanced variable size HOG features

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

5 Scopus citations

Abstract

In this paper, we proposed an enhanced variable size HOG feature based on the boosting framework. The proposed feature utilizes the information which is ignored in quantization gradient orientation that only using one orientation to encode each pixel. Furthermore, we utilized a fixed Gaussian template to convolve with the integral orientation histograms in order to interpolate the weight of each pixel from its surroundings. Either of the two steps have an important effect on the discriminative ability of HOG feature which leads to increase the detection rate. Soft cascade framework is utilized to train our final human detector. The experiment result based on INRIA database shows that our proposed feature improves the detection rate about 5% at the false positive per window rate of 10-4 compared to the original feature.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages342-349
Number of pages8
EditionPART 2
DOIs
StatePublished - 2011
Externally publishedYes
Event8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China
Duration: 29 May 20111 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6676 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Symposium on Neural Networks, ISNN 2011
Country/TerritoryChina
CityGuilin
Period29/05/111/06/11

Keywords

  • enhanced integral HOG feature
  • human detection
  • integral HOG feature
  • soft cascade

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