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http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1293
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DC Field | Value | Language |
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dc.contributor.author | Kokul, T. | |
dc.contributor.author | Fookes, C. | |
dc.contributor.author | Sridharan, C. | |
dc.contributor.author | Ramanan, A. | |
dc.contributor.author | Pinidiyaarachchi, U.A.J. | |
dc.date.accessioned | 2019-11-25T07:14:27Z | |
dc.date.accessioned | 2022-06-27T04:11:22Z | - |
dc.date.available | 2019-11-25T07:14:27Z | |
dc.date.available | 2022-06-27T04:11:22Z | - |
dc.date.issued | 2017-09-17 | |
dc.identifier.issn | 2381-8549 | |
dc.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1293 | - |
dc.description.abstract | Convolutional neural networks (CNNs) have been employedin visual tracking due to their rich levels of feature representation.While the learning capability of a CNN increaseswith its depth, unfortunately spatial information is diluted indeeper layers which hinders its important ability to localize targets. To successfully manage this trade-off, we propose anovel residual network based gating CNN architecture for objecttracking. Our deep model connects the front and bottomconvolutional features with a gate layer. This new networklearns discriminative features while reducing the spatial informationlost. This architecture is pre-trained to learn generictracking characteristics. In online tracking, an efficient domainadaptation mechanism is used to accurately learn thetarget appearance with limited samples. Extensive evaluationperformed on a publicly available benchmark dataset demonstratesour proposed tracker outperforms state-of-the-art approaches. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE, IEEE International Conference on Image Processing (ICIP) | en_US |
dc.subject | object tracking | en_US |
dc.subject | domain adaptation | en_US |
dc.subject | CNN | en_US |
dc.title | Gate connected convolutional neural network for object tracking | en_US |
dc.type | Article | en_US |
Appears in Collections: | Physical Science |
Files in This Item:
File | Description | Size | Format | |
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Gate connected convolutional neural network for object tracking.pdf | 52.86 kB | Adobe PDF | View/Open |
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