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http://dx.doi.org/10.5909/JBE.2018.23.2.186

Robust Online Object Tracking via Convolutional Neural Network  

Gil, Jong In (Department of Computer and Communications Eng., Kangwon National University)
Kim, Manbae (Department of Computer and Communications Eng., Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.23, no.2, 2018 , pp. 186-196 More about this Journal
Abstract
In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.
Keywords
visual tracking; convolutional neural network; on-line tracking; probability map; color histogram;
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