Please use this identifier to cite or link to this item:
http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1293
Title: | Gate connected convolutional neural network for object tracking |
Authors: | Kokul, T. Fookes, C. Sridharan, C. Ramanan, A. Pinidiyaarachchi, U.A.J. |
Keywords: | object tracking;domain adaptation;CNN |
Issue Date: | 17-Sep-2017 |
Publisher: | IEEE, IEEE International Conference on Image Processing (ICIP) |
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. |
URI: | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1293 |
ISSN: | 2381-8549 |
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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