A novel part-based approach to mean-shift algorithm for visual tracking

Jae Pil Hwang, Jeonghyun Baek, Baehoon Choi, Euntai Kim

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Visual tracking is one of the most important problems considered in computer vision. To improve the performance of the visual tracking, a part-based approach will be a good solution. In this paper, a novel method of visual tracking algorithm named part-based mean-shift (PBMS) algorithm is presented. In the proposed PBMS, unlike the standard mean-shift (MS), the target object is divided into multiple parts and the target is tracked by tracking each individual part and combining the results. For the part-based visual tracking, the objective function in the MS is modified such that the target object is represented as a combination of the parts and iterative optimization solution is presented. Further, the proposed PBMS provides a systematic and analytic way to determine the scale of the bounding box for the target from the perspective of the objective function optimization. Simulation is conducted with several benchmark problems and the result shows that the proposed PBMS outperforms the standard MS.

Original languageEnglish
Pages (from-to)443-453
Number of pages11
JournalInternational Journal of Control, Automation and Systems
Volume13
Issue number2
DOIs
Publication statusPublished - 2015 Apr 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

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