Vision based pedestrian detection using histogram of oriented gradients, Adaboost & linear support vector machines

Authors

Samantha D. F. Hilado, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila
Laurence A. Gan Lim, De La Salle University, Manila
Edwin Sybingco, De La Salle University, Manila
Isidro V. Marfori, De La Salle University, Manila
Alvin Y. Chua, De La Salle University, Manila

College

Gokongwei College of Engineering

Department/Unit

Mechanical Engineering

Document Type

Conference Proceeding

Source Title

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Publication Date

12-1-2012

Abstract

Pedestrian detection systems are valuable in a variety of applications such as in advanced driver assistance systems and advanced robots. This study presents a pedestrian detection system that uses Histogram of Oriented Gradients (HOG) as feature descriptor, and AdaBoost and Linear Support Vector Machines (SVM) as classifiers. The entire system is tested and evaluated in both publicly available databases and personally acquired videos. The pedestrian detection system has been tested and results show that it can detect pedestrians. Experiments showed that the system is up 20% faster compared to OpenCV's default detector. © 2012 IEEE.

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Digitial Object Identifier (DOI)

10.1109/TENCON.2012.6412236

Recommended Citation

Hilado, S. F., Dadios, E. P., Gan Lim, L. A., Sybingco, E., Marfori, I. V., & Chua, A. Y. (2012). Vision based pedestrian detection using histogram of oriented gradients, Adaboost & linear support vector machines. IEEE Region 10 Annual International Conference, Proceedings/TENCON https://doi.org/10.1109/TENCON.2012.6412236

Disciplines

Mechanical Engineering

Keywords

Boosting (Algorithms); Pedestrian traffic flow; Pedestrians

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