Learning on lie groups for invariant detection and tracking

Oncel Tuzel, Fatih Porikli

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

70 Scopus citations

Abstract

This paper presents a novel learning based tracking model combined with object detection. The existing techniques proceed by linearizing the motion, which makes an implicit Euclidean space assumption. Most of the transformations used in computer vision have matrix Lie group structure. We learn the motion model on the Lie algebra and show that the formulation minimizes a first order approximation to the geodesic error. The learning model is extended to train a class specific tracking function, which is then integrated to an existing pose dependent object detector to build a pose invariant object detection algorithm. The proposed model can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing object detection methods. The detection rate of the original detector is improved by more than 90% for large transformations.

Original languageEnglish (US)
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Country/TerritoryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Learning on lie groups for invariant detection and tracking'. Together they form a unique fingerprint.

Cite this