Robust dual-model object tracking with camera in motion

Xiaodong Cai, F. H. Ali, E. Stipidis

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

This paper proposes a robust dual-model matching and model update algorithm for object tracking with camera in motion. With the proposed technique, a short-term model is matched then updated each frame when it is matched to reflect the most recent changes of the tracked object in illumination, size and deformation. Furthermore, a long-term model is maintained in a period of time then updated in a lower frequency to against the interference of rapid and temporal change of the above factors. In addition, a statistic-based sub-region update strategy rather than global update for both short-term and long-term tracking models is utilized. The proposed algorithm is intensity-based, but it can be extended to any feature matching, such as color, texture and shape appearance. An embedded version of this algorithm has been implemented with Texas Instruments' DM6446-based DSP system. Extensive experiments and practical applications in different situations confirm the robustness and reliability of the proposed method.

Original languageEnglish
Title of host publication3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009
Volume2009
Edition2
DOIs
Publication statusPublished - 1 Dec 2009
Event3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009 - London, United Kingdom
Duration: 3 Dec 20093 Dec 2009

Conference

Conference3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009
CountryUnited Kingdom
CityLondon
Period3/12/093/12/09

Keywords

  • Embedded
  • Illumination change
  • Occlusion
  • Tracking

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