Real-time pedestrian detection using support vector machines

Seonghoon Kang, Hyeran Byun, Seong Whan Lee

    Research output: Contribution to journalArticlepeer-review

    16 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

    Bibliographical note

    Funding Information:
    ∗This research was supported Technology, Korea. †Author for correspondence.

    Copyright:
    Copyright 2008 Elsevier B.V., All rights reserved.

    Keywords

    • Pedestrian detection
    • Stereo vision
    • Support vector machines

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence

    Fingerprint

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

    Cite this