Pedestrian detection with an ensemble of localized features

Shaopeng Tang, Satoshi Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, a new human detection approach from still image is proposed. Two vector features are extracted from the image. Histogram of oriented Gradient feature represents the gradient information of human. Histogram of modified local binary pattern is extracted from images convolved with Gabor filter, as a feature vector to represent texture information. It can be seen as a supplement of gradient information. Different support vector machine classifiers are trained by each type of vectors. Finally, two classifiers are combined together for the final result by using the proposed integration method. Because two features contain different information, they have low error dependency and can get high detection rate. Experiment is performed in a large dataset and it shows that this method outperforms state-of-the-art approaches and other combinations of features.

Original languageEnglish
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Pages2838-2841
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei
Duration: 2009 May 242009 May 27

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
CityTaipei
Period09/5/2409/5/27

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Tang, S., & Goto, S. (2009). Pedestrian detection with an ensemble of localized features. In Proceedings - IEEE International Symposium on Circuits and Systems (pp. 2838-2841). [5118393] https://doi.org/10.1109/ISCAS.2009.5118393