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
EditorsSeong-Whan Lee, Alessandro Verri
PublisherSpringer Verlag
Pages268-277
Number of pages10
ISBN (Print)354044016X
DOIs
Publication statusPublished - 2002 Jan 1
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)0302-9743
ISSN (Electronic)1611-3349

Other

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

Fingerprint

Pedestrian Detection
Face recognition
Support vector machines
Support Vector Machine
Real-time
Face Detection
Obstacle Detection
Binary Classification
Object Detection
Face Recognition
Guidance
Segmentation
Vertical
Necessary
Experiments
Demonstrate
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kang, S., Byun, H., & Lee, S. W. (2002). Real-time pedestrian detection using support vector machines. In S-W. Lee, & A. Verri (Eds.), Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings (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
Kang, Seonghoon ; Byun, Hyeran ; Lee, Seong Whan. / Real-time pedestrian detection using support vector machines. Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings. editor / Seong-Whan Lee ; Alessandro Verri. Springer Verlag, 2002. pp. 268-277 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Kang, S, Byun, H & Lee, SW 2002, Real-time pedestrian detection using support vector machines. in S-W Lee & A Verri (eds), Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2388, Springer Verlag, pp. 268-277, 1st International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, Niagara Falls, Canada, 02/8/10. https://doi.org/10.1007/3-540-45665-1_21

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

Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings. ed. / Seong-Whan Lee; Alessandro Verri. Springer Verlag, 2002. p. 268-277 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2388).

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

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