3D object tracking using mean-shift and similarity-based aspect-graph modeling

Jwu-Sheng Hu*, Tzung Min Su, Chung Wei Juan, George Wang

*Corresponding author for this work

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

2 Scopus citations

Abstract

The mean shift algorithm is a popular method in the field of 2D object tracking due to its simplicity and robustness over slight variations of lighting condition, scale and view-point over time. However, the appearance of 3D object might have distinctive variations for different viewpoints over time. In this work, a novel method for tracking 3D objects using mean-shift algorithm and a 3D object database is proposed to achieve a more precise tracking. A 3D object database using similarity-based aspect-graph is built from 2D images sampled at random intervals from the viewing sphere. Contour and color features of each 2D image are used for modeling the 3D object database. To conduct tracking, a suitable object model is selected from the database and the mean-shift tracking is applied to find the local minima of a similarity measure between the color histograms of the object model and the target image. The effectiveness of the proposed method is demonstrated by experiments with objects rotating and translating in space.

Original languageEnglish
Title of host publicationProceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
Pages2383-2388
Number of pages6
DOIs
StatePublished - 1 Dec 2007
Event33rd Annual Conference of the IEEE Industrial Electronics Society, IECON - Taipei, Taiwan
Duration: 5 Nov 20078 Nov 2007

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Conference

Conference33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
CountryTaiwan
CityTaipei
Period5/11/078/11/07

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