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Real-time pedestrian detection using support vector machines
Seonghoon Kang
, Hyeran Byun
,
Seong Whan Lee
*
*
Corresponding author for this work
Research output
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Contribution to journal
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Article
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peer-review
16
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Keyphrases
Support Vector Machine
100%
Real-time Pedestrian Detection
100%
Detection Method
66%
Face Detection
66%
Pedestrian Detection
66%
Foreground Object
33%
Face Recognition
33%
Stereo
33%
Outdoor Environment
33%
Feature Extracting
33%
Guidance System
33%
Obstacle Detection
33%
Torso
33%
Binary Classification
33%
Object Detection
33%
Outdoor Scenes
33%
Pedestrian
33%
Experiment Test
33%
Vertical Edge
33%
Edge Feature
33%
Visually Impaired
33%
Walking Guidance
33%
Computer Science
Detection Method
100%
Face Detection
100%
Support Vector Machine
100%
Foreground Object
50%
Face Recognition
50%
Classification Problem
50%
Extracted Feature
50%
obstacle detection
50%
Object Detection
50%
Binary Classification
50%
Candidate Region
50%
Engineering
Support Vector Machine
100%
Foreground Object
50%
Test Result
50%
Classification Problem
50%
Guidance System
50%
Major Part
50%
Extracted Feature
50%
Earth and Planetary Sciences
Real Time
100%
Support Vector Machine
100%
Detection Method
66%
Outdoor Environment
33%
Agricultural and Biological Sciences
Support Vector Machine
100%
Face
100%
Leg
50%