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http://dx.doi.org/10.6109/jkiice.2021.25.3.389

Target Image Exchange Model for Object Tracking Based on Siamese Network  

Park, Sung-Jun (School of Electronics and Information Engineering, Korea Aerospace University)
Kim, Gyu-Min (School of Electronics and Information Engineering, Korea Aerospace University)
Hwang, Seung-Jun (School of Electronics and Information Engineering, Korea Aerospace University)
Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.3, 2021 , pp. 389-395 More about this Journal
Abstract
In this paper, we propose a target image exchange model to improve performance of the object tracking algorithm based on a Siamese network. The object tracking algorithm based on the Siamese network tracks the object by finding the most similar part in the search image using only the target image specified in the first frame of the sequence. Since only the object of the first frame and the search image compare similarity, if tracking fails once, errors accumulate and drift in a part other than the tracked object occurs. Therefore, by designing a CNN(Convolutional Neural Network) based model, we check whether the tracking is progressing well, and the target image exchange timing is defined by using the score output from the Siamese network-based object tracking algorithm. The proposed model is evaluated the performance using the VOT-2018 dataset, and finally achieved an accuracy of 0.611 and a robustness of 22.816.
Keywords
Object tracking; Deep learning; Siamese network; Convolutional neural network;
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