Model-based temporal object verification using video

Baoxin Li, Rama Chellappa, Qinfen Zheng, Sandor Z. Der

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

An approach to model-based dynamic object verification and identification using video is proposed. From image sequences containing the moving object, we compute its motion trajectory. Then we estimate its three-dimensional (3-D) pose at each time step. Pose estimation is formulated as a search problem, with the search space constrained by the motion trajectory information of the moving object and assumptions about the scene structure. A generalized Hausdorff metric, which is more robust to noise and allows a confidence interpretation, is suggested for the matching procedure used for pose estimation as well as the identification and verification problem. The pose evolution curves are used to assist in the acceptance or rejection of an object hypothesis. The models are acquired from real image sequences of the objects. Edge maps are extracted and used for matching. Results are presented for both infrared and optical sequences containing moving objects involved in complex motions.

Original languageEnglish (US)
Pages (from-to)897-908
Number of pages12
JournalIEEE Transactions on Image Processing
Volume10
Issue number6
DOIs
StatePublished - Jun 1 2001
Externally publishedYes

Keywords

  • Hausdorff matching
  • Moving object recognition
  • Object recognition
  • Video processing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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