Abstract
We treat tracking as a binary classification task in order to distinguish between an object to be tracked and the background. We propose to integrate an online learning based total-error-rate minimization method (OTER) with an observation model of particle filter for visual tracking. The particle filter is modeled using an affine dynamic model and an observation model. The observation model is constructed using the OTER classifier for binary pattern classification. The proposed method is empirically evaluated both qualitatively and quantitatively using several publicly available video sequences.
| Original language | English |
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| Title of host publication | ICICS 2011 - 8th International Conference on Information, Communications and Signal Processing |
| DOIs | |
| Publication status | Published - 2011 Dec 1 |
| Event | 8th International Conference on Information, Communications and Signal Processing, ICICS 2011 - Singapore, Singapore Duration: 2011 Dec 13 → 2011 Dec 16 |
Other
| Other | 8th International Conference on Information, Communications and Signal Processing, ICICS 2011 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 11/12/13 → 11/12/16 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
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Visual tracking with online discriminative learning. / Jang, Se In; Choi, Kwontaeg; Kim, Youngsung; Oh, Beom Seok; Toh, Kar Ann.
ICICS 2011 - 8th International Conference on Information, Communications and Signal Processing. 2011. 6173536.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Visual tracking with online discriminative learning
AU - Jang, Se In
AU - Choi, Kwontaeg
AU - Kim, Youngsung
AU - Oh, Beom Seok
AU - Toh, Kar Ann
PY - 2011/12/1
Y1 - 2011/12/1
N2 - We treat tracking as a binary classification task in order to distinguish between an object to be tracked and the background. We propose to integrate an online learning based total-error-rate minimization method (OTER) with an observation model of particle filter for visual tracking. The particle filter is modeled using an affine dynamic model and an observation model. The observation model is constructed using the OTER classifier for binary pattern classification. The proposed method is empirically evaluated both qualitatively and quantitatively using several publicly available video sequences.
AB - We treat tracking as a binary classification task in order to distinguish between an object to be tracked and the background. We propose to integrate an online learning based total-error-rate minimization method (OTER) with an observation model of particle filter for visual tracking. The particle filter is modeled using an affine dynamic model and an observation model. The observation model is constructed using the OTER classifier for binary pattern classification. The proposed method is empirically evaluated both qualitatively and quantitatively using several publicly available video sequences.
UR - http://www.scopus.com/inward/record.url?scp=84860627354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860627354&partnerID=8YFLogxK
U2 - 10.1109/ICICS.2011.6173536
DO - 10.1109/ICICS.2011.6173536
M3 - Conference contribution
SN - 9781457700309
BT - ICICS 2011 - 8th International Conference on Information, Communications and Signal Processing
ER -