Abstract
In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.
| Original language | English |
|---|---|
| Article number | 6739132 |
| Pages (from-to) | 1288-1300 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Circuits and Systems for Video Technology |
| Volume | 24 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
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Keywords
- Illumination change
- kernel-based tracking
- local binary pattern
- mean shift
- visual object tracking
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Media Technology
Cite this
Kernel-based structural binary pattern tracking. / Kim, Dae Hwan; Kim, Hyo Kak; Lee, Seung Jun; Park, Won Jae; Ko, Sung-Jea.
In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 8, 6739132, 01.01.2014, p. 1288-1300.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Kernel-based structural binary pattern tracking
AU - Kim, Dae Hwan
AU - Kim, Hyo Kak
AU - Lee, Seung Jun
AU - Park, Won Jae
AU - Ko, Sung-Jea
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.
AB - In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.
KW - Illumination change
KW - kernel-based tracking
KW - local binary pattern
KW - mean shift
KW - visual object tracking
UR - http://www.scopus.com/inward/record.url?scp=84905685580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905685580&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2014.2305514
DO - 10.1109/TCSVT.2014.2305514
M3 - Article
AN - SCOPUS:84905685580
VL - 24
SP - 1288
EP - 1300
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
SN - 1051-8215
IS - 8
M1 - 6739132
ER -