Real-Time Pedestrian Detection Using Support Vector Machines

  • Seonghoon Kang
  • Hyeran Byun
  • Seong-Whan Lee
Conference paper

DOI: 10.1007/3-540-45665-1_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2388)
Cite this paper as:
Kang S., Byun H., Lee SW. (2002) Real-Time Pedestrian Detection Using Support Vector Machines. In: Lee SW., Verri A. (eds) Pattern Recognition with Support Vector Machines. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Seonghoon Kang
    • 1
  • Hyeran Byun
    • 2
  • Seong-Whan Lee
    • 1
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulKorea
  2. 2.Department of Computer ScienceYonsei UniversiySeoulKorea

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