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 journalArticle

8 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

Fingerprint

FIR filters
Maximum likelihood
Noise
Likelihood Functions
Kalman filters
Uncertainty
IIR filters
Experiments

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

Cite this

Maximum likelihood FIR filter for visual object tracking. / Min Pak, Jung; Ahn, Choon Ki; Hak Mo, Yung; Lim, Myo Taeg; Kyou Song, Moon.

In: Neurocomputing, Vol. 216, 05.12.2016, p. 543-553.

Research output: Contribution to journalArticle

Min Pak, Jung ; Ahn, Choon Ki ; Hak Mo, Yung ; Lim, Myo Taeg ; Kyou Song, Moon. / Maximum likelihood FIR filter for visual object tracking. In: Neurocomputing. 2016 ; Vol. 216. pp. 543-553.
@article{2f1b4667c782428496d29af9e3629eee,
title = "Maximum likelihood FIR filter for visual object tracking",
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.",
keywords = "Finite impulse response (FIR) filter, Maximum likelihood, Maximum likelihood FIR filter (MLFIRF), Visual object tracking (VOT)",
author = "{Min Pak}, Jung and Ahn, {Choon Ki} and {Hak Mo}, Yung and Lim, {Myo Taeg} and {Kyou Song}, Moon",
year = "2016",
month = "12",
day = "5",
doi = "10.1016/j.neucom.2016.07.047",
language = "English",
volume = "216",
pages = "543--553",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

TY - JOUR

T1 - Maximum likelihood FIR filter for visual object tracking

AU - Min Pak, Jung

AU - Ahn, Choon Ki

AU - Hak Mo, Yung

AU - Lim, Myo Taeg

AU - Kyou Song, Moon

PY - 2016/12/5

Y1 - 2016/12/5

N2 - 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.

AB - 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.

KW - Finite impulse response (FIR) filter

KW - Maximum likelihood

KW - Maximum likelihood FIR filter (MLFIRF)

KW - Visual object tracking (VOT)

UR - http://www.scopus.com/inward/record.url?scp=84994121203&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994121203&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2016.07.047

DO - 10.1016/j.neucom.2016.07.047

M3 - Article

AN - SCOPUS:84994121203

VL - 216

SP - 543

EP - 553

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -