Human detection by a small autonomous mobile robot

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

Abstract

We propose a human detection method using HOG and SVM from an image by a small autonomous mobile robot. Existing works for human detection from images cannot be used for our purpose because they assume that the images are taken from a high position, at least at the height of a small human child, while our robot is of 15cm height. The combination of HOG and SVM is known as the most successful human detection method so we adopt it. To cope with a wide variety of human shapes mainly due to the distance to them, we devised a two stage prediction method which uses two kinds of SVM classifiers based on an estimation of the distance. The estimation is based on the ratio of the skin color pixel in the image, which allows us to clearly separate our problem into whole body detection and partial body detection. Experiments in an office showed promising results of our method with F value 0.93.

Original languageEnglish
Title of host publicationExtraction et Gestion des Connaissances, EGC 2012
Pages233-238
Number of pages6
Publication statusPublished - Dec 1 2012
Event12th Journees Internationales Francophones Extraction et Gestion des Connaissances, EGC 2012 - 12th French-speaking International Conference on Knowledge Discovery and Management, EGC 2012 - Bordeaux, France
Duration: Jan 31 2012Feb 3 2012

Publication series

NameRevue des Nouvelles Technologies de l'Information
VolumeE.23
ISSN (Print)1764-1667

Other

Other12th Journees Internationales Francophones Extraction et Gestion des Connaissances, EGC 2012 - 12th French-speaking International Conference on Knowledge Discovery and Management, EGC 2012
CountryFrance
CityBordeaux
Period1/31/122/3/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

Fingerprint Dive into the research topics of 'Human detection by a small autonomous mobile robot'. Together they form a unique fingerprint.

Cite this