Evaluation of local feature descriptors and their combination for pedestrian representation

Jixiang Liang*, Qixiang Ye, Jie Chen, Jianbin Jiao

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Pedestrian detection problem has been a touchstone of various image feature descriptors. In this paper, we evaluate four kinds of representative local descriptors (HOG, Haar-like, SURF and LBP) for pedestrian representation. Our goal is to find out the best combination of feature descriptors by analyzing and evaluating the complementarities of them. With the cross validation method, we first find out the best descriptor, which is then combined with other descriptors one by one for evaluation. In addition to direct descriptor combination, we propose a new descriptor strategy, called structural combination. Experiments on two public pedestrian datasets show that the performance evaluation can support the complementarily analysis and the complementarities is relevant to combination strategies.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2496-2499
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
Country/TerritoryJapan
CityTsukuba
Period11/11/1215/11/12

Keywords

  • feature complementarities
  • feature representation
  • Pedestrian detection

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