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http://dx.doi.org/10.9717/kmms.2022.25.2.247

Object Feature Tracking Algorithm based on Siame-FPN  

Kim, Jong-Chan (Dept. of Computer Engineering, Sunchon National University)
Lim, Su-Chang (Dept. of Computer Engineering, Sunchon National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.2, 2022 , pp. 247-256 More about this Journal
Abstract
Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.
Keywords
Computer Vision; Convolutional Neural Networks; Deep Learning; Object Tracking; Siamese Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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