Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

Ying Yang; Yu Gang Ma; Xiao Dong Guo; Kun Jiao

doi:10.4028/www.scientific.net/AEF.2-3.433

Paper Titles

Bipedal Walking Trajectory Generation Using Tchebychev Method
p.414

A Sludge Cleaning Robot for Sulfuric Acid Tank
p.419

Research on EEG-Based Control of Prosthetic Hand
p.423

Human-Robot Interaction System Research for 500kV EHV Power Transmission Line Inspection Robot
p.427

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features
p.433

Design of Vibrating Signal Acquisition and Processing System Based on Wireless Transducer Network
p.441

Twin Channel of Speed Control System Based on DSP
p.447

Development of a Wireless Inertial Measurement System for Pitching Motion Analysis
p.452

The Design of Embedded AeroEngine Measurement and Test System Based on ARM and FPGA
p.458

HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 2-3Research on Pedestrian’s Detection Based on the...

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

Abstract:

In this Paper, Propose a Pedestrian Detection Method that Based on Adaboost Algorithm and Pedestrian Shape Features Integration. First According to the Collected Pedestrian True, False Sample, Selected the Characteristics of the Extended Class Haar, Adopt Adaboost Algorithm Training Get Pedestrian Classifier to Split the Initial Candidate Region of All Pedestrians in the Image. in this Paper, Propose an Adaptive Threshold Weight Update Method, Significantly Reduced the Number of the Characteristics of Strong Classifier, Optimize the Classifier Structure, Reduce the Complexity of the Algorithm; Meanwhile, the Online Update Detector, Improving the Reliability of the Detector. Pedestrian Leg Have Strong Vertical Edge Symmetry Characteristic so that Extracted the Vertical Edge Detection in the Initial Candidate Region, According to the Symmetry Determine the Vertical Axis of Symmetry, Combined with the Morphological Characteristics of Pedestrians to Determine the Width and Height Characteristics of the Pedestrian, to Determine the Pedestrian Candidate Region, Finally, Put a Further Validation to the Pedestrian Candidate Region.

By email Full Text Pdf

You have full access to the following eBook

Read eBook

Info:

Periodical:

Advanced Engineering Forum (Volumes 2-3)

Pages:

433-438

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.2-3.433

Citation:

Cite this paper

Online since:

December 2011

Authors:

Ying Yang, Yu Gang Ma, Xiao Dong Guo, Kun Jiao

Keywords:

Adaboost Algorithm, Classifier, Edge Detection, Pedestrian Detection

Export:

RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

References

[1] M. Bertozzi,A. Broggi,P. Lombardi. Artificial Vision in Road Vehicles[J]. Proceddings of the IEEE, July 2002, 90(7) : 1258-1271.

DOI: 10.1109/jproc.2002.801444

Google Scholar

[2] M. Szarvas,A. Yoshizawa, M. Yamamoto and J. Ogata. Pedestrian Detection with Convolutional Neural Networks[C], Proc. IEEE Conference on Intelligent Transportation Systems, Tokyo, Japan, 2005: 224-229.

DOI: 10.1109/ivs.2005.1505106

Google Scholar

[3] F. Xu and K. Fujimura. Pedestrian Detection and Tracking with Night Vision[J], IEEE Transactions Intelligent Transportation Systems, March 2005, 6(1): 63-71.

DOI: 10.1109/tits.2004.838222

Google Scholar

[4] Massimo Bertozzi, Alberto Broggi. Pedestrian Detection for Driver Assistance Using Multiresolution Infrared Vision[J], IEEE Transcations on Vehicular Technology, Novermber 2004, 53(6): 1666-1678.

DOI: 10.1109/tvt.2004.834878

Google Scholar

[5] M. Bertozzi, A. Broggi,M. Carletti,A. Fascioli, T. Graf, P. Grisleri, and M. Meinecke. IR pedestrian detection for advanced driver assistance systems[J], Lecture Notes Computer Science, 2003, 2781: 582-590.

DOI: 10.1007/978-3-540-45243-0_74

Google Scholar

[6] D.M. Gavrila. Sensor-based pedestrian protection[J], IEEE Intelligent Systems, 2001, 16(6): 77-81.

DOI: 10.1109/5254.972097

Google Scholar

[7] Wang Liang, Hu Weiming and Tan Tieniu, status identify based on tread[J], Computer Transaction, 2003, 26(3)：353-360.

Google Scholar

[8] Liang Wang, Huazhong Ning, Tieniu Tan and Weiming Hu. Fusion of Static and Dynamic Body Biometrics for Gait Recognition[J], IEEE Transcations on circuits and systems for Video Technology, February 2004, 14(2): 149-158.

DOI: 10.1109/iccv.2003.1238660

Google Scholar

[9] Tian Guang, Qi Feihu and Zhu Wenjia, Walking people identify by single camera [J], System simulation transaction, 2006, 18(10): 2906-2910.

Google Scholar

[10] Dan Witzner Hansen, John Paulin Hansen, Mads Nielsen, Anders Sewerin, Johansen and Mikkel B. Stegmann. Eye Typing using Markov and Active Appearance Models. IEEE Workshop on Applications on Computer Vision, 2002, pp . 132-136.

DOI: 10.1109/acv.2002.1182170

Google Scholar

[11] Kong Fanzhi, Zhang Xingzhou and Xie Yaoju, the technic of human face based on Adaboost[J], Application of science and technology, 2005, 32(6): 7-9.

Google Scholar

[12] Bertozzi M, Broggi A, Fascioli A, Graf T, Meinecke M M Pedestrian detection for driver assistance using multiresolution infrared vision. IEEE Transactions on Vehicular Technology, 2004, 53(6): 1666-1678.

DOI: 10.1109/tvt.2004.834878

Google Scholar