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Hybrid Efficient Convolution Operators for Visual Tracking

Yu Wang*

Department of Computer Science, Harbin Institute of Technology, Weihai, 264200, China

* Corresponding Author: Yu Wang. Email: email

Journal on Artificial Intelligence 2021, 3(2), 63-72. https://doi.org/10.32604/jai.2021.010455

Abstract

Visual tracking is a classical computer vision problem with many applications. Efficient convolution operators (ECO) is one of the most outstanding visual tracking algorithms in recent years, it has shown great performance using discriminative correlation filter (DCF) together with HOG, color maps and VGGNet features. Inspired by new deep learning models, this paper propose a hybrid efficient convolution operators integrating fully convolution network (FCN) and residual network (ResNet) for visual tracking, where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects, respectively. Compared with the traditional VGGNet, our approach has higher accuracy for dealing with the issues of segmentation and image size. The experiments show that our approach would obtain better performance than ECO in terms of precision plot and success rate plot on OTB-2013 and UAV123 datasets.

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Cite This Article

APA Style
Wang, Y. (2021). Hybrid efficient convolution operators for visual tracking. Journal on Artificial Intelligence, 3(2), 63-72. https://doi.org/10.32604/jai.2021.010455
Vancouver Style
Wang Y. Hybrid efficient convolution operators for visual tracking. J Artif Intell . 2021;3(2):63-72 https://doi.org/10.32604/jai.2021.010455
IEEE Style
Y. Wang, "Hybrid Efficient Convolution Operators for Visual Tracking," J. Artif. Intell. , vol. 3, no. 2, pp. 63-72. 2021. https://doi.org/10.32604/jai.2021.010455



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