Dynamically visual learning for people identification with sparsely distributed cameras

Hidenori Tanaka, Itaru Kitahara, Hideo Saito, Hiroshi Murase, Kiyoshi Kogure, Norihiro Hagita

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

We propose a dynamic visual learning method that aims to identify people by using sparsely distributed multiple surveillance cameras. In the proposed method, virtual viewpoint images are synthesized by interpolating the sparsely distributed images with a simple 3D shape model of the human head, so that virtual densely distributed multiple images can be obtained. The multiple images generate an initial eigenspace in the initial learning step. In the following additional learning step, other distributed cameras capture additional images that update the eigenspace to improve the recognition performance. The discernment capability for personal identification of the proposed method is demonstrated experimentally.

Original languageEnglish
Pages (from-to)130-140
Number of pages11
JournalLecture Notes in Computer Science
Volume3540
DOIs
Publication statusPublished - 2005
Event14th Scandinavian Conference on Image Analysis, SCIA 2005 - Joensuu, Finland
Duration: 2005 Jun 192005 Jun 22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Dynamically visual learning for people identification with sparsely distributed cameras'. Together they form a unique fingerprint.

Cite this