Real-time buried threat detection and cueing capability in VPEF environment

Ling, Bo; Agarwal, Sanjeev; Olivera, Santiago; Vasilkoski, Zlatko; Phan, Chung; Geyer, Chris

doi:10.1117/12.2177743

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…EO/IR-based systems are widely used for the detection of potential threats [ 19 , 20 ]. Compared to the normal imaging sensor, EO/IR can detect the target even in the darkness of night.…”

Section: Related Workmentioning

confidence: 99%

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

Kim

Khan

Bohak

et al. 2018

Sensors

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A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.

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“…EO/IR-based systems are widely used for the detection of potential threats [ 19 , 20 ]. Compared to the normal imaging sensor, EO/IR can detect the target even in the darkness of night.…”

Section: Related Workmentioning

confidence: 99%

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

Kim

Khan

Bohak

et al. 2018

Sensors

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“…Team member MIGMA built a vehicular buried threat detection system using a set of modules with baseline detection, a suite of False Alarm Mitigators connected with AND fusion and target tracking that were converted to VPEF plug-ins built using the VPEF SDK and the VPEF-RT framework 8 . A sustained framerate of 4-5 FPS was demonstrated running a MIGMA's detection pipeline.…”

Section: End-to-end Exploitation With Vpef Sdk and Vpef-rtmentioning

confidence: 99%

Near real-time, on-the-move software PED using VPEF

Green¹,

Geyer²,

Burnette³

et al. 2015

SPIE Proceedings

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The scope of the Micro-Cloud for Operational, Vehicle-Based EO-IR Reconnaissance System (MOVERS) development effort, managed by the Night Vision and Electronic Sensors Directorate (NVESD), is to develop, integrate, and demonstrate new sensor technologies and algorithms that improve improvised device/mine detection using efficient and effective exploitation and fusion of sensor data and target cues from existing and future Route Clearance Package (RCP) sensor systems. Unfortunately, the majority of forward looking Full Motion Video (FMV) and computer vision processing, exploitation, and dissemination (PED) algorithms are often developed using proprietary, incompatible software. This makes the insertion of new algorithms difficult due to the lack of standardized processing chains. In order to overcome these limitations, EOIR developed the Government off-theshelf (GOTS) Video Processing and Exploitation Framework (VPEF) to be able to provide standardized interfaces (e.g., input/output video formats, sensor metadata, and detected objects) for exploitation software and to rapidly integrate and test computer vision algorithms. EOIR developed a vehicle-based computing framework within the MOVERS and integrated it with VPEF. VPEF was further enhanced for automated processing, detection, and publishing of detections in near real-time, thus improving the efficiency and effectiveness of RCP sensor systems.

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Real-time buried threat detection and cueing capability in VPEF environment

Cited by 2 publications

References 10 publications

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

Near real-time, on-the-move software PED using VPEF

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