Advances in Target Detection and Tracking in  Forward-Looking InfraRed (FLIR) Imagery

Sanna, Andrea; Lamberti, Fabrizio

doi:10.3390/s141120297

Cited by 25 publications

(14 citation statements)

References 11 publications

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“…The background interference included in the infrared maritime image is mainly composed of island and wave. In infrared images, the intensity of an object mainly depends on its temperature and radiated heat and it is not influenced by light conditions and object surface features [33]. Next, we will analyze the features of these two kinds of interference.…”

Section: Analysis Of Background Interference Featuresmentioning

confidence: 99%

A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

Dong

Zhang

et al. 2020

Sensors

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Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments.

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Section: Analysis Of Background Interference Featuresmentioning

confidence: 99%

A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

Dong

Zhang

et al. 2020

Sensors

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“…Anti-jamming e-measures Data provenance Must track to sensor point in time configuration and metadata Fabric Analytics for security intelligence Security software intelligence-event driven and monitoring-that is often remote Event detection For example, target identification in a video stream infers height of target from shadow. Fuse data from satellite infrared with separate sensor stream [231].…”

Section: Security Against Dos Attacksmentioning

confidence: 99%

NIST Big Data Interoperability Framework:

Subgroup.¹

2019

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NIST Special Publication series 1500 is intended to capture external perspectives related to NIST standards, measurement, and testing-related efforts. These external perspectives can come from industry, academia, government, and others. These reports are intended to document external perspectives and do not represent official NIST positions. Certain commercial entities, equipment, or materials may be identified in this document to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, federal agencies may wish to closely follow the development of these new publications by NIST. Organizations are encouraged to review all draft publications during public comment periods and provide feedback to NIST. All NIST publications are available at http://www.nist.gov/publication-portal.cfm.

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“…Small target detection on infrared (IR) imagery is one of the basic, yet challenging problems of infrared vision. Depending on the detection range, target size, clutter level, operational success requirements, computation power availability, and power constraints, several solutions [1][2][3] have been proposed for various systems, such as infrared search-and-track (IRST), forward-looking infrared (FLIR), just to name a few. Although there are countless approaches to the problem, an efficient, widely accepted, and off-the-shelf solution still does not exist for small target detection or recognition problem on infrared imagery.…”

Section: Introductionmentioning

confidence: 99%

Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

Demir¹,

Akagündüz²

2020

Turk J Elec Eng & Comp Sci

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In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similar to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on midwave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.

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Advances in Target Detection and Tracking in Forward-Looking InfraRed (FLIR) Imagery

Cited by 25 publications

References 11 publications

A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

NIST Big Data Interoperability Framework:

Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

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