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 |
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Pages (from-to) | 543-553 |
Number of pages | 11 |
Journal | Neurocomputing |
Volume | 216 |
DOIs | |
Publication status | Published - 2016 Dec 5 |
Bibliographical note
Funding Information:This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2016R1D1A1B01016071 ), in part by Basic Science Research Program through the NRF funded by the Ministry of Science, ICT, and Future Planning (Grant No. NRF-2014R1A1A1006101 ), and in part by Basic Science Research Program through the NRF funded by the Ministry of Education (Grant No. NRF-2013R1A1A2060663 ).
Publisher Copyright:
© 2016 Elsevier B.V.
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