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 language | English |
|---|---|
| Pages (from-to) | 543-553 |
| Number of pages | 11 |
| Journal | Neurocomputing |
| Volume | 216 |
| DOIs | |
| Publication status | Published - 2016 Dec 5 |
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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 journal › Article
}
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 -