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DC Field | Value | Language |
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dc.contributor.author | Zhou, H | - |
dc.contributor.author | Fei, M | - |
dc.contributor.author | Sadka, A | - |
dc.contributor.author | Zhang, Y | - |
dc.contributor.author | Li, X | - |
dc.date.accessioned | 2015-10-13T16:23:26Z | - |
dc.date.available | 2014-11 | - |
dc.date.available | 2015-10-13T16:23:26Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Pattern Recognition, 47 (11): 3552 - 3567, ( 2014) | en_US |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0031320314001915 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/11487 | - |
dc.description.abstract | Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets. | en_US |
dc.description.sponsorship | The work of H. Zhou is supported in part by UK EPSRC (Grant EP/J006238/1) and Invest NI. X. Li is supported by the National Natural Science Foundation of China (Grant No: 61125106) and Shaanxi Key Innovation Team of Science and Technology (Grant No: 2012KCT-04). | en_US |
dc.format.extent | 3552 - 3567 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Object tracking | en_US |
dc.subject | Occlusion | en_US |
dc.subject | Colour | en_US |
dc.subject | Motion energy | en_US |
dc.title | Adaptive fusion of particle filtering and spatio-temporal motion energy for human tracking | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.patcog.2014.05.006 | - |
dc.relation.isPartOf | Pattern Recognition | - |
pubs.issue | 11 | - |
pubs.publication-status | Published | - |
pubs.publication-status | Published | - |
pubs.publication-status | Published | - |
pubs.publication-status | Published | - |
pubs.volume | 47 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 2.74 MB | Adobe PDF | View/Open |
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