Deep GoogLeNet Features for Visual Object Tracking
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391 citations
Cites methods from "Deep GoogLeNet Features for Visual ..."
...The used deep transfer learning CNN models investigated in this research are Alexnet [29], Resnet18 [39], Googlenet [60], The mentioned CNN models had a few numbers of layers if it is compared to large CNN models such as Xception [40], Densenet [42], and Inceptionresnet [61] which consist of 71, 201 and 164 layers accordingly....
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Cites result from "Deep GoogLeNet Features for Visual ..."
...From the Table 1 it is evident that the proposed idea with only 2 samples (one from each class) we were able to achieve 100% detection rate and the same has been compared with well-known deep learning models such as AlexNet [24], GoogLeNet [25] and ResNet [26]....
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References
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"Deep GoogLeNet Features for Visual ..." refers background in this paper
...Deep convolutional neural networks have clearly shown excellent performance in object recognition and object detection problems [6], [7], [21], and are therefore of interest for visual object tracking....
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"Deep GoogLeNet Features for Visual ..." refers methods in this paper
...Some of the popularly used features are Histogram of Oriented gradients (HOG) [16], Color names [13] and CNN features [8]–[10]....
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...Feature representations such as HOG [16], Color Names etc. [13], [18] have been successfully employed in DCF based tracking frameworks....
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...Till 2015, most of the trackers used the hand-crafted appearance features, such as HOG and color names for modelling the target object....
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...Feature representations such as HOG [16], Color Names etc....
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...Some of the popularly used features are Histogram of Oriented gradients (HOG) [16], Color names [13] and CNN features [8]–[10]....
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