Abstract

Detecting and estimating the presence and pose of a person in an image is a challenging problem. Literature has dealt with this as two separate problems. In this paper, we propose a system that introduces novel steps to segment the foreground object from the back ground and classifies the pose of the detected human as frontal, profile or back view. We use this as a front end to an intelligent environment we are developing to assist individuals who are blind in office spaces. The traditional background subtraction often results in silhouettes that are discontinuous, containing holes. We have incorporated the graph cut algorithm on top of background subtraction result and have observed a significant improvement in the performance of segmentation yielding continuous silhouettes without any holes. We then extract shape context features from the silhouette for training a classifier to distinguish between profile and nonprofile(frontal or back) views. Our system has shown promising results by achieving an accuracy of 87.5% for classifying profile and non profile views using an SVM on the real data sets that we have collected for our experiments.

Original languageEnglish (US)
Title of host publicationVISAPP 2007 - Proceedings of the 2nd International Conference on Computer Vision Theory and Applications
Pages137-142
Number of pages6
VolumeIU
EditionMTSV/-
StatePublished - 2007
Event2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain
Duration: Mar 8 2007Mar 11 2007

Other

Other2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007
CountrySpain
CityBarcelona
Period3/8/073/11/07

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Keywords

  • Graph cut
  • Human detection
  • Shape context
  • SVM

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Krishnan, N. C., Li, B., & Panchanathan, S. (2007). Detecting and classifying frontal, back and profile views of humans. In VISAPP 2007 - Proceedings of the 2nd International Conference on Computer Vision Theory and Applications (MTSV/- ed., Vol. IU, pp. 137-142)