Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

doi:10.5302/j.icros.2009.15.4.379

Journal of Institute of Control, Robotics and Systems

2009

DOI: 10.5302/j.icros.2009.15.4.379

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Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

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2011

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A Study on the Pedestrian Detection on the Road Using Machine Vision

Lee¹,

Truong²,

Kim³

et al. 2011

Journal of Institute of Control, Robotics and Systems

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In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.

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A Study on the Pedestrian Detection on the Road Using Machine Vision

Lee¹,

Truong²,

Kim³

et al. 2011

Journal of Institute of Control, Robotics and Systems

View full text Add to dashboard Cite

show abstract

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Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

Cited by 1 publication

References 60 publications

A Study on the Pedestrian Detection on the Road Using Machine Vision

A Study on the Pedestrian Detection on the Road Using Machine Vision

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