Maximum likelihood FIR filter for visual object tracking

Jung Min Pak, Choon Ki Ahn, Yung Hak Mo, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Visual object trackers usually adopt filters, such as the Kalman filter (KF) and the particle filter (PF), in order to improve tracking accuracy by suppressing measurement noises. However, if the filters have infinite impulse response (IIR) structures, the visual trackers adopting them can exhibit degraded tracking performance when system models have parameter uncertainties or when the noise information is incorrect. To overcome this problem, in this paper, we propose a new finite impulse response (FIR) filter for visual object tracking (VOT). The proposed filter is derived by maximizing the likelihood function, and it is referred to as the maximum likelihood FIR filter (MLFIRF). We conducted extensive experiments to show that the MLFIRF provides superior and more reliable tracking results compared with the KF, PF, and H filter (HF) in VOT.

Original languageEnglish
Pages (from-to)543-553
Number of pages11
JournalNeurocomputing
Volume216
DOIs
Publication statusPublished - 2016 Dec 5

Keywords

  • Finite impulse response (FIR) filter
  • Maximum likelihood
  • Maximum likelihood FIR filter (MLFIRF)
  • Visual object tracking (VOT)

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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