Real-time pedestrian detection using support vector machines

Seonghoon Kang, Hyeran Byun, Seong Whan Lee

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.

Original languageEnglish
Pages (from-to)405-416
Number of pages12
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number3
DOIs
Publication statusPublished - 2003 May 1

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Face recognition
Support vector machines
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

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Real-time pedestrian detection using support vector machines. / Kang, Seonghoon; Byun, Hyeran; Lee, Seong Whan.

In: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 17, No. 3, 01.05.2003, p. 405-416.

Research output: Contribution to journalArticle

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