Object tracking based on an online learning network with total error rate minimization

Se In Jang, Kwontaeg Choi, Kar Ann Toh, Andrew Beng Jin Teoh, Jaihie Kim

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.

Original languageEnglish
Pages (from-to)126-139
Number of pages14
JournalPattern Recognition
Issue number1
Publication statusPublished - 2015 Jan 1

Bibliographical note

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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