Estimation-correction scheme based articulated object tracking using SIFT features and mean shift algorithm

Ying Lu, Chengjiao Guo, Takeshi Ikenaga

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

7 Citations (Scopus)

Abstract

Object tracking plays an important role in video surveillance system. However, in the field of object tracking, complex object motion and object occlusions still remains challenging topics. This paper proposes a Estimation-Correction (EC) object tracking scheme in real scenarios, combining the strength of scale invariant feature transform (SIFT) and mean shift algorithm. The corresponding SIFT features are used to estimate the position of the target candidate by the scale and space relation between each pair of features. Then mean shift is applied to conduct the local similarity search so as to find a right position and size of estimated candidate with a maximum likelihood. Experiment results demonstrate that the proposed SIFT/mean shift strategy keeps the tracking error in average 8 pixels and improves the tracking performance compared with the traditional SIFT and mean shift algorithm when tracking objects with complex motion and full occlusion.

Original languageEnglish
Title of host publicationNISS2010 - 4th International Conference on New Trends in Information Science and Service Science
Pages275-280
Number of pages6
Publication statusPublished - 2010 Oct 18
Event4th International Conference on New Trends in Information Science and Service Science, NISS2010 - Gyeongju, Korea, Republic of
Duration: 2010 May 112010 May 13

Publication series

NameNISS2010 - 4th International Conference on New Trends in Information Science and Service Science

Conference

Conference4th International Conference on New Trends in Information Science and Service Science, NISS2010
CountryKorea, Republic of
CityGyeongju
Period10/5/1110/5/13

Keywords

  • Articulated object tracking
  • Mean shift algorithm
  • Sift features

ASJC Scopus subject areas

  • Information Systems and Management

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