Change detection on UGV patrols with respect to a reference tour using VIS imagery

Müller, Thomas

doi:10.1117/12.2182663

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Real-time mobile automated systems for the detection of small objects have been explored in the past. Specifically for IED detection various techniques may be used, such as LiDAR [2], ground-based monocular camera imagery [3][4][5], ground-based stereo imagery [6,7], or imagery from unmanned aerial vehicles [8]. What these methods have in common is that they search for small environmental changes compared to imagery captured at an earlier moment in time.…”

Section: Isandt Membermentioning

confidence: 99%

An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms

Klomp¹,

Wouw²

2020

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Section: Isandt Membermentioning

confidence: 99%

An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms

Klomp¹,

Wouw²

2020

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“…and calculate a structural adaptive image difference ∆p i between the current image p i and pi . In this procedure, of which the first part was successfully used before for moving target indication (MTI) 25 and change detection on autonomous unmanned ground vehicles (UGVs), 26 for each pixel p i (x, y) the local intensity dynamic is registered. This is done by calculating the minimal and maximal intensity in the direct pixel neighborhood of size four leading to an interval I(x, y) modeling the local intensity dynamic:…”

Section: Structural Adaptive Image Differencingmentioning

confidence: 99%

Robust drone detection for day/night counter-UAV with static VIS and SWIR cameras

Müller

2017

SPIE Proceedings

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Recent progress in the development of unmanned aerial vehicles (UAVs) has led to more and more situations in which drones like quadrocopters or octocopters pose a potential serious thread or could be used as a powerful tool for illegal activities. Therefore, counter-UAV systems are required in a lot of applications to detect approaching drones as early as possible. In this paper, an efficient and robust algorithm is presented for UAV detection using static VIS and SWIR cameras. Whereas VIS cameras with a high resolution enable to detect UAVs in the daytime in further distances, surveillance at night can be performed with a SWIR camera. First, a background estimation and structural adaptive change detection process detects movements and other changes in the observed scene. Afterwards, the local density of changes is computed used for background density learning and to build up the foreground model which are compared in order to finally get the UAV alarm result. The density model is used to filter out noise effects, on the one hand. On the other hand, moving scene parts like moving leaves in the wind or driving cars on a street can easily be learned in order to mask such areas out and suppress false alarms there. This scene learning is done automatically simply by processing without UAVs in order to capture the normal situation. The given results document the performance of the presented approach in VIS and SWIR in different situations.

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“…Real-time automated systems for the detection of IED cues or markers have been explored in the past, such as by using LiDAR systems [4], ground-based monocular camera imagery [5][6][7], ground-based stereo imagery [8] [9], or imagery from unmanned aerial vehicles [10]. All methods have in common that they search for small environmental changes compared to a previous patrol, under the assumption that the placement of an IED causes small appearance changes in the environment.…”

Section: Introductionmentioning

confidence: 99%

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

Klomp¹,

Wouw²,

B.V.³

et al. 2019

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Change detection on UGV patrols with respect to a reference tour using VIS imagery

Cited by 5 publications

References 15 publications

An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms

An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms

Robust drone detection for day/night counter-UAV with static VIS and SWIR cameras

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

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