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      Anti-Interference Aircraft-Tracking Method in Infrared Imagery †

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          Abstract

          In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers.

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          Most cited references62

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          Object Tracking Benchmark.

          Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
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            High-Speed Tracking with Kernelized Correlation Filters

            The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.
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              Particle filters for positioning, navigation, and tracking

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 March 2019
                March 2019
                : 19
                : 6
                : 1289
                Affiliations
                [1 ]School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China; singlechip@ 123456163.com (K.Z.); jyan@ 123456nwpu.edu.cn (J.Y.)
                [2 ]Shanghai Aerospace Control Technology Institute, Shanghai 201109, China; nssycit@ 123456163.com
                Author notes
                [* ]Correspondence: wusij@ 123456mail.nwpu.edu.cn ; Tel.: +86-178-2902-0145
                [†]

                This paper is an extended version of Sijie Wu, Saisai Niu, Kai Zhang and Jie Yan’s paper ”An Aircraft Tracking Method in Simulated Infrared Image Sequences Based on Regional Distribution.” published in Pacific-Rim Symposium on Image and Video Technology, 2017.

                Author information
                https://orcid.org/0000-0001-5659-0458
                Article
                sensors-19-01289
                10.3390/s19061289
                6470981
                30875773
                8b2b2a1d-d712-4e7a-99d9-8da1acfc0d67
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 January 2019
                : 11 March 2019
                Categories
                Article

                Biomedical engineering
                aircraft tracking,regional distribution,occlusion detection,simulated infrared image

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