Mean-Shift Object Tracking with Discrete and Real AdaBoost Techniques
- Authors
- Baskoro, Hendro; Kim, Jun-Seong; Kim, Chang-Su
- Issue Date
- 6월-2009
- Publisher
- WILEY
- Keywords
- Mean-shift; blob tracking; object tracking; adaptive boosting (AdaBoost); likelihood image
- Citation
- ETRI JOURNAL, v.31, no.3, pp.282 - 291
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ETRI JOURNAL
- Volume
- 31
- Number
- 3
- Start Page
- 282
- End Page
- 291
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/119919
- DOI
- 10.4218/etrij.09.0108.0372
- ISSN
- 1225-6463
- Abstract
- An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.