Abstract
We propose a robust visual tracking system, which refines initial estimates of a base tracker by employing object proposal techniques. First, we decompose the base tracker into three building blocks: Representation method, appearance model, and model update strategy. We then design each building block by adopting and improving ideas from recent successful trackers. Second, we propose the proposal-guided tracking (PGT) algorithm. Given proposals generated by an edge-based object proposal technique, we select only the proposals that can improve the result of the base tracker using several cues. Then, we discriminate target proposals from non-target ones, based on the nearest neighbor classification using the target and background models. Finally, we replace the result of the base tracker with the best target proposal. Experimental results demonstrate that proposed PGT algorithm provides excellent results on a visual tracking benchmark.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1762-1767 |
| Number of pages | 6 |
| Volume | 2018-February |
| ISBN (Electronic) | 9781538615423 |
| DOIs | |
| Publication status | Published - 2018 Feb 5 |
| Event | 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia Duration: 2017 Dec 12 → 2017 Dec 15 |
Other
| Other | 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 |
|---|---|
| Country | Malaysia |
| City | Kuala Lumpur |
| Period | 17/12/12 → 17/12/15 |
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Information Systems
- Signal Processing
Cite this
PGT : Proposal-guided object tracking. / Kim, Han Ul; Kim, Chang-Su.
Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. p. 1762-1767.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - PGT
T2 - Proposal-guided object tracking
AU - Kim, Han Ul
AU - Kim, Chang-Su
PY - 2018/2/5
Y1 - 2018/2/5
N2 - We propose a robust visual tracking system, which refines initial estimates of a base tracker by employing object proposal techniques. First, we decompose the base tracker into three building blocks: Representation method, appearance model, and model update strategy. We then design each building block by adopting and improving ideas from recent successful trackers. Second, we propose the proposal-guided tracking (PGT) algorithm. Given proposals generated by an edge-based object proposal technique, we select only the proposals that can improve the result of the base tracker using several cues. Then, we discriminate target proposals from non-target ones, based on the nearest neighbor classification using the target and background models. Finally, we replace the result of the base tracker with the best target proposal. Experimental results demonstrate that proposed PGT algorithm provides excellent results on a visual tracking benchmark.
AB - We propose a robust visual tracking system, which refines initial estimates of a base tracker by employing object proposal techniques. First, we decompose the base tracker into three building blocks: Representation method, appearance model, and model update strategy. We then design each building block by adopting and improving ideas from recent successful trackers. Second, we propose the proposal-guided tracking (PGT) algorithm. Given proposals generated by an edge-based object proposal technique, we select only the proposals that can improve the result of the base tracker using several cues. Then, we discriminate target proposals from non-target ones, based on the nearest neighbor classification using the target and background models. Finally, we replace the result of the base tracker with the best target proposal. Experimental results demonstrate that proposed PGT algorithm provides excellent results on a visual tracking benchmark.
UR - http://www.scopus.com/inward/record.url?scp=85050464181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050464181&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2017.8282318
DO - 10.1109/APSIPA.2017.8282318
M3 - Conference contribution
AN - SCOPUS:85050464181
VL - 2018-February
SP - 1762
EP - 1767
BT - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -