Multi-part non-rigid object tracking based on time model-space gradients

T. Nunomaki, S. Yonemoto, D. Arita, R. Taniguchi, N. Tsuruta

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

1 Citation (Scopus)


This paper presents a shape and pose estimation method for 3D multi-part objects, the purpose of which is to easily map objects from the real world into virtual environments. In general, complex 3D multi-part objects cause undesired self-occlusion and non-rigid motion. To deal with the problem, we assume the following constraints: – object model is represented in a tree structure consisting of deformable parts. – connected parts are articulated at one point (called “articulation point”). – as a 3D parametric model of the parts, we employ deformable superquadrics (we call DSQ). To estimate the parameters from the sensory data, we use time modelspace gradient method, which reduces the parameter estimation problem into solving a simultaneous linear equation. We have demonstrated that our system works well for multiple-part objects using real image data.

Original languageEnglish
Title of host publicationArticulated Motion and Deformable Objects - 1st International Workshop, AMDO 2000, Proceedings
EditorsHans-Hellmut Nagel, Francisco J. Perales Lopez
PublisherSpringer Verlag
Number of pages11
ISBN (Print)354067912X
Publication statusPublished - 2000
Event1st International Workshop on Articulated Motion and Deformable Objects, AMDO 2000 - Palma de Mallorca, Spain
Duration: Sept 7 2000Sept 9 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Workshop on Articulated Motion and Deformable Objects, AMDO 2000
CityPalma de Mallorca

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Multi-part non-rigid object tracking based on time model-space gradients'. Together they form a unique fingerprint.

Cite this