Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/77448
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dc.contributor.author | Li, X. | - |
dc.contributor.author | Dick, A. | - |
dc.contributor.author | Shen, C. | - |
dc.contributor.author | Zhang, Z. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.contributor.author | Wang, H. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2013; 22(8):3028-3040 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.issn | 1941-0042 | - |
dc.identifier.uri | http://hdl.handle.net/2440/77448 | - |
dc.description.abstract | A key problem in visual tracking is how to effectively combine spatio-temporal visual information from throughout a video to accurately estimate the state of an object. We address this problem by incorporating Dempster-Shafer information fusion into the tracking approach. To implement this fusion task, the entire image sequence is partitioned into spatially and temporally adjacent subsequences. A support vector machine (SVM) classifier is trained for object=non-object classification on each of these subsequences, the outputs of which act as separate data sources. To combine the discriminative information from these classifiers, we further present a spatio-temporal weighted Dempster-Shafer (STWDS) scheme. Moreover, temporally adjacent sources are likely to share discriminative information on object/non-object classification. In order to use such information, an adaptive SVM learning scheme is designed to transfer discriminative information across sources. Finally, the corresponding Dempster-Shafer belief function of the STWDS scheme is embedded into a Bayesian tracking model. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracking approach. | - |
dc.description.statementofresponsibility | Xi Li, Anthony Dick, Chunhua Shen, Zhongfei Zhang, Anton van den Hengel, Hanzi Wang | - |
dc.language.iso | en | - |
dc.publisher | IEEE-Inst Electrical Electronics Engineers Inc | - |
dc.rights | Copyright (c) 2013 IEEE. | - |
dc.source.uri | http://dx.doi.org/10.1109/tip.2013.2253478 | - |
dc.subject | Dempster- Shafer information fusion | - |
dc.subject | Visual tracking | - |
dc.subject | adaptive SVM learning | - |
dc.subject | appearance model | - |
dc.subject | multi-source discriminative learning | - |
dc.title | Visual tracking with spatio-temporal Dempster-Shafer information fusion | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TIP.2013.2253478 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP1094764 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP1094764 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Dick, A. [0000-0001-9049-7345] | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_77448.pdf | Accepted version | 2.16 MB | Adobe PDF | View/Open |
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