Specific Shape Feature for Fast Pedestrian Detection in Cascade Way

Yuqing He, Tao Yang, Ya Lu, Mingqi Liu, Xiangyang Zhai

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

1 Citation (Scopus)

Abstract

A113 various effective pedestrian detection methods have been put forward, it is still a challenging work to detect people with accuracy and speed. This paper proposed a fast pedestrian detector with novel features and effective classifier. Firstly, the shape based luminance comparison (SLC) features were proposed, which can represent the image information well, and compute fast. Secondly, the random trees classifier was trained in a cascade way, which also can reduce compute time. Additionally, several implementation strategies were employed to improve the performance of our detector. This method is evaluated on challenging dataset which demonstrated some improvements to the state of the art of pedestrian detector method.

Original languageEnglish
Title of host publicationProceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
EditorsBing Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages335-339
Number of pages5
ISBN (Electronic)9781538618035
DOIs
Publication statusPublished - 20 Sept 2018
Event2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 - Xi'an, China
Duration: 25 May 201827 May 2018

Publication series

NameProceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

Conference

Conference2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Country/TerritoryChina
CityXi'an
Period25/05/1827/05/18

Keywords

  • cascade
  • pedestrian detection
  • random trees classifier
  • shape based luminance comparison

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