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http://dx.doi.org/10.9717/kmms.2022.25.1.072

Bottleneck-based Siam-CNN Algorithm for Object Tracking  

Lim, Su-Chang (Dept. of Computer Engineering, Sunchon National University)
Kim, Jong-Chan (Dept. of Computer Engineering, Sunchon National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.1, 2022 , pp. 72-81 More about this Journal
Abstract
Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.
Keywords
Deep Learning; Computer Vision; Object Tracking; Convolutional Neural Networks; Siamese Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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