Pedestrian Detection with Stereo Vision

Authors:
Mineki Soga;Takeo Kato;Mitsuhiko Ohta;Yoshiki Ninomiya
Affiliations:
Toyota Central R&D Labs., Inc., Sensing System Lab.;Toyota Central R&D Labs., Inc., Sensing System Lab.;Toyota Central R&D Labs., Inc., Sensing System Lab.;Toyota Central R&D Labs., Inc., Sensing System Lab.
Venue:
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Year:
2005

Citing 0
Cited 5

Moving obstacle detection in highly dynamic scenes

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
2D-3D-based on-board pedestrian detection system

Computer Vision and Image Understanding
Integrated video motion estimator with Retinex-like pre-processing for robust motion analysis in automotive scenarios: algorithmic and real-time architecture design

Journal of Real-Time Image Processing
Pedestrian recognition in far-infrared images by combining boosting-based detection and skeleton-based stochastic tracking

PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Pedestrian detection on moving vehicle using stereovision and 2d cue

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method of pedestrian detection for automobile applications, based on stereo vision. Vision based pedestrian detection presents a difficulty due to the diversity of appearances. The proposed method overcomes the difficulty mainly through the following two contributions. Firstly, employing four directional features with the classifier increases the robustness against small affine deformation of objects. Secondly, by merging classification and tracking, robustness against temporal change of appearance is improved when considering temporal continuity of classification score. The experiments, performed on approximately 16 minutes of video sequences, confirmed that the method can detect pedestrians with low false detection.