An LVQ-based technique for human motion segmentation

M. Hariadi, A. Harada, T. Aoki, T. Higuchi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


This paper describes a novel approach for human motion segmentation from digital color video sequences. The problem is to separate the human image as target object from its background image in a color video sequence. In our approach, every pixel of a video frame is considered to be a 5-dimensional vector consisting of x-y coordinate components plus 3 color components in HSV color space. The basic idea is to use learning vector quantization (LVQ) defined in 5-dimensional vector space to distinguish the target human object from its background image. We assume that the target human object and its background are classified by hand at the first frame. The initial classification data are used to train the system for generating the initial codebook vectors. These codebook vectors define class regions in the 5-dimensional vector space. For tracking the target human object class in succeeding frames, LVQ codebook vectors are updated periodically by feeding back the result of classification into the training step. This paper also presents performance evaluation of the proposed LVQ-based segmentation algorithm.

Original languageEnglish
Title of host publicationProceedings - APCCAS 2002
Subtitle of host publicationAsia-Pacific Conference on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0780376900
Publication statusPublished - 2002
Externally publishedYes
EventAsia-Pacific Conference on Circuits and Systems, APCCAS 2002 - Denpasar, Bali, Indonesia
Duration: 28 Oct 200231 Oct 2002

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS


ConferenceAsia-Pacific Conference on Circuits and Systems, APCCAS 2002
CityDenpasar, Bali


  • Background noise
  • Colored noise
  • Computer vision
  • Humans
  • Motion segmentation
  • Noise shaping
  • Shape
  • Vector quantization
  • Video compression
  • Video sequences


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