File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ISCAS.2014.6865187
- Scopus: eid_2-s2.0-84907399094
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: A new visual object tracking algorithm using Bayesian Kalman filter
Title | A new visual object tracking algorithm using Bayesian Kalman filter |
---|---|
Authors | |
Keywords | Object tracking Baysian Kalman filter Mean shift |
Issue Date | 2014 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089 |
Citation | The 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1-5 June 2014. In IEEE International Symposium on Circuits and Systems Proceedings, 2014, p. 522-525 How to Cite? |
Abstract | This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEE |
Persistent Identifier | http://hdl.handle.net/10722/204109 |
ISBN | |
ISSN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, S | en_US |
dc.contributor.author | Chan, SC | en_US |
dc.contributor.author | Liao, B | en_US |
dc.contributor.author | Tsui, KM | en_US |
dc.date.accessioned | 2014-09-19T20:06:06Z | - |
dc.date.available | 2014-09-19T20:06:06Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.citation | The 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1-5 June 2014. In IEEE International Symposium on Circuits and Systems Proceedings, 2014, p. 522-525 | en_US |
dc.identifier.isbn | 978-1-4799-3432-4 | - |
dc.identifier.issn | 0271-4302 | - |
dc.identifier.uri | http://hdl.handle.net/10722/204109 | - |
dc.description.abstract | This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEE | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089 | en_US |
dc.relation.ispartof | IEEE International Symposium on Circuits and Systems Proceedings | en_US |
dc.subject | Object tracking | - |
dc.subject | Baysian Kalman filter | - |
dc.subject | Mean shift | - |
dc.title | A new visual object tracking algorithm using Bayesian Kalman filter | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Chan, SC: ascchan@hku.hk | en_US |
dc.identifier.email | Liao, B: binliao@hku.hk | en_US |
dc.identifier.email | Tsui, KM: kmtsui11@hku.hk | en_US |
dc.identifier.authority | Chan, SC=rp00094 | en_US |
dc.identifier.authority | Tsui, KM=rp00181 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ISCAS.2014.6865187 | en_US |
dc.identifier.scopus | eid_2-s2.0-84907399094 | - |
dc.identifier.hkuros | 239784 | en_US |
dc.identifier.spage | 522 | en_US |
dc.identifier.epage | 525 | en_US |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 141006 | - |
dc.identifier.issnl | 0271-4302 | - |