Real-time pedestrian detection using support vector machines

Seonghoon Kang, Hyeran Byun, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we present a real-time pedestrian detection system in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system. It can discriminate pedestrian from obstacles, and extract candidate regions for face detection and recognition. For pedestrian detection, we have used stereo-based segmentation and SVM (Support Vector Machines), which has superior classification performance in binary classification case (e. g. object detection). We have used vertical edges, which can extracted from arms, legs, and the body of pedestrians, as features for training and detection. The experiments on a large number of street scenes demonstrate the effectiveness of the proposed for pedestrian detection system.

Original languageEnglish
Title of host publicationPattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings
PublisherSpringer Verlag
Pages268-277
Number of pages10
Volume2388
ISBN (Print)354044016X
DOIs
Publication statusPublished - 2002
Event1st International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002 - Niagara Falls, Canada
Duration: 2002 Aug 102002 Aug 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2388
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002
CountryCanada
CityNiagara Falls
Period02/8/1002/8/10

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Real-time pedestrian detection using support vector machines'. Together they form a unique fingerprint.

  • Cite this

    Kang, S., Byun, H., & Lee, S. W. (2002). Real-time pedestrian detection using support vector machines. In Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings (Vol. 2388, pp. 268-277). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2388). Springer Verlag. https://doi.org/10.1007/3-540-45665-1_21