Paper Title
Exploration of Deterministic Sampling for Convolutional Neural Networks Tracker for Single Object Tracking
Abstract
This paper presents a deterministic approach to train and update object appearance model for a convolutional neural
networks (CNNs)tracker. A network of three convolutional and three fully connected layers is used to model the object of
interest appearance based on solely the initialization frame information. The proposed sampling method is only given the
ground truth location of the object on the first frame. Positive training samples are extracted by translating the ground truth
with varying step size in eight directions. Meanwhile, negative training samples are extracted from the eight neighborhood
regions of equal size that surround the foreground object. Then, the output sample is selected by combining the highest samples
based on combine score of its appearance similarity. The experimental test shown that the proposed approach can provide an
alternative solution to train and update the object appearance model using CNNs tracker.
Index Terms - Convolutional neural network, Deep learning, Deterministic sampling, Visual object tracking.
Author - Nur Ayuni Mohamed, Mohd Asyraf Zulkifley
Published : Volume-6,Issue-4 ( Apr, 2019 )
DOIONLINE Number - IJAECS-IRAJ-DOIONLINE-15366
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Published on 2019-06-24 |
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