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Open AccessFeature PaperArticle

A Robust Structured Tracker Using Local Deep Features

Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 846;
Received: 30 March 2020 / Revised: 21 April 2020 / Accepted: 14 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers. View Full-Text
Keywords: convolutional neural networks; convex optimization; visual target tracking convolutional neural networks; convex optimization; visual target tracking
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Javanmardi, M.; Farzaneh, A.H.; Qi, X. A Robust Structured Tracker Using Local Deep Features. Electronics 2020, 9, 846.

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