&lt;title&gt;Pose-independent automatic target detection and recognition using 3D LADAR data&lt;/title&gt;

Vasile, Alexandru N.; Marino, Richard M.

doi:10.1117/12.546761

Cited by 32 publications

(36 citation statements)

References 21 publications

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“…In this way the classification system will be more robust. Finally target features must be correlated with data from other sensors such as radar (Vasile and Marino, 2005) or sonar.…”

Section: Resultsmentioning

confidence: 99%

Ship Classification From Multispectral Videos

2012

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Surveillance of a seaport can be achieved by different means: radar, sonar, cameras, radio communications and so on. Such a surveillance aims, on the one hand, to manage cargo and tanker traffic, and, on the other hand, to prevent terrorist attacks in sensitive areas. In this paper an application to video-surveillance of a seaport entrance is presented, and more particularly, the different steps enabling to classify mobile shapes. This classification is based on a parameter measuring the similarity degree between the shape under study and a set of reference shapes. The classification result describes the considered mobile in terms of shape and speed, as speed is determined by target tracking.

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“…In this way the classification system will be more robust. Finally target features must be correlated with data from other sensors such as radar (Vasile and Marino, 2005) or sonar.…”

Section: Resultsmentioning

confidence: 99%

Ship Classification From Multispectral Videos

2012

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“…The application was based on geometrically invariant relationships. A pose-independent automatic target detection and recognition system, which used data from an airborne 3D imaging LADAR sensor, was presented by Vasile et al [19]. This automatic target recognition system uses geometric shape and size signatures from a target model to detect and recognise a target amid extensive terrain scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Goztepe¹

2013

sajie

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During combat, measuring the dimensions of targets is extremely important for knowing when to fire on the enemy. The importance of identifying a known target on land emphasizes the importance of techniques devoted to automatic target recognition. Although a number of object-recognition techniques have been developed in the past, none of them have provided the desired specifics for unidentified target recognition. Studies on target recognition are largely based on images that assume that images of a known target can be readily viewed under any circumstance. But this is not true for military operations conducted on various terrains under specific circumstances. Usually it is not possible to capture images of unidentified objects because of weather, inadequate equipment, or concealment. In this study, a new approach that integrates neural networks and laser radar has been developed for automatic target recognition in order to reduce the abovementioned problems. Unlike current studies, the proposed model uses the geometric dimensions of unidentified targets in order to detect and recognise them under severe weather conditions. OPSOMMINGDie bepaling van teikenafmetings is van besondere belanggedurende gevegte sodat vuurtydstipte sodoende vasgelê kan word. In hierdie opsig word outomatiese uitkenning van teikentipe dus ook belangrik. Laasgenoemde tegnieke het desnieteenstaande nie besonder presteer met die uitkenning van vreemdeteikentipes nie. Terreintoestande, weersomstandighede, swak waarnemingstoerusting en kamoeflering speel in die verband ook 'n rol. Nuwerwetse toerusting wat gebruikmaak van neurale netwerke laserradar word voorgehou as 'n oplossing vir die vraagstuk onder uiteenlopende omgewingstoestande.

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“…1.5 points/m 2 ). In [10,16,30,31], there are typically several hundreds of samples on a target. Typically, the data is collected in a forward-looking perspective, while in [4] and [28] down-looking perspective data is considered.…”

Section: Related Workmentioning

confidence: 99%

“…Vehicle recognition using laser radar Several ATR methods or systems for recognition of military ground vehicles based on laser radar data have been proposed over the years [4,10,30,31,33,35]. During the last years, also ATR of civilian personal cars, mainly for traf c monitoring, have been proposed [16,28,34].…”

Section: Related Workmentioning

confidence: 99%

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Ground Target Recognition Using Rectangle Estimation

Grönwall

Gustafsson

Millnert

2006

IEEE Trans. on Image Process.

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We propose a ground target recognition method based on 3D laser radar data. The method handles general 3D scattered data. It is based on the fact that manmade objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the targets functional/main parts and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.Keywords: Rectangle estimation, laser radar, automatic target recognition 1 Ground target recognition using rectangle estimation Christina Grönwall, Fredrik Gustafsson, Mille Millnert Abstract-We propose a ground target recognition method based on 3D laser radar data. The method handles general 3D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identi cation of segments that represent the target's functional/main parts and target matching with CAD models.The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

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<title>Pose-independent automatic target detection and recognition using 3D LADAR data</title>

Cited by 32 publications

References 21 publications

Ship Classification From Multispectral Videos

Ship Classification From Multispectral Videos

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Ground Target Recognition Using Rectangle Estimation

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