The Experts below are selected from a list of 2679 Experts worldwide ranked by ideXlab platform
Henrik Petersson - One of the best experts on this subject based on the ideXlab platform.
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AVSS - Motion compensation of submillimeter wave 3D imaging radar data for Security Screening
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017Co-Authors: Maria Axelsson, Mikael Karlsson, Henrik PeterssonAbstract:Security systems at airports, borders, and other public areas require high throughput Screening at the same time as a high level of protection against possible threats is achieved. New safe stand-off systems with automatic detection of concealed objects that operate in the submillimeter wave region are being developed to meet the need for fast Screening and respected privacy. These new technologies include radar 3D imaging of moving persons. Real-time fame rates are desired in these scanning radar 3D imaging systems to achieve high throughput. High throughput can also be achieved at lower near real-time frame rates if the 3D images can be motion compensated before detection of concealed objects. In this paper we present a method for motion compensation of submillimeter wave 3D imaging radar data used for Security Screening. We demonstrate the method on simulated 3D radar data with known motion errors with good results.
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Motion compensation of submillimeter wave 3D imaging radar data for Security Screening
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017Co-Authors: Maria Axelsson, Mikael Karlsson, Henrik PeterssonAbstract:Security systems at airports, borders, and other public areas require high throughput Screening at the same time as a high level of protection against possible threats is achieved. New safe stand-off systems with automatic detection of concealed objects that operate in the submillimeter wave region are being developed to meet the need for fast Screening and respected privacy. These new technologies include radar 3D imaging of moving persons. Real-time fame rates are desired in these scanning radar 3D imaging systems to achieve high throughput. High throughput can also be achieved at lower near real-time frame rates if the 3D images can be motion compensated before detection of concealed objects. In this paper we present a method for motion compensation of submillimeter wave 3D imaging radar data used for Security Screening. We demonstrate the method on simulated 3D radar data with known motion errors with good results.
Maria Axelsson - One of the best experts on this subject based on the ideXlab platform.
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AVSS - Motion compensation of submillimeter wave 3D imaging radar data for Security Screening
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017Co-Authors: Maria Axelsson, Mikael Karlsson, Henrik PeterssonAbstract:Security systems at airports, borders, and other public areas require high throughput Screening at the same time as a high level of protection against possible threats is achieved. New safe stand-off systems with automatic detection of concealed objects that operate in the submillimeter wave region are being developed to meet the need for fast Screening and respected privacy. These new technologies include radar 3D imaging of moving persons. Real-time fame rates are desired in these scanning radar 3D imaging systems to achieve high throughput. High throughput can also be achieved at lower near real-time frame rates if the 3D images can be motion compensated before detection of concealed objects. In this paper we present a method for motion compensation of submillimeter wave 3D imaging radar data used for Security Screening. We demonstrate the method on simulated 3D radar data with known motion errors with good results.
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Motion compensation of submillimeter wave 3D imaging radar data for Security Screening
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017Co-Authors: Maria Axelsson, Mikael Karlsson, Henrik PeterssonAbstract:Security systems at airports, borders, and other public areas require high throughput Screening at the same time as a high level of protection against possible threats is achieved. New safe stand-off systems with automatic detection of concealed objects that operate in the submillimeter wave region are being developed to meet the need for fast Screening and respected privacy. These new technologies include radar 3D imaging of moving persons. Real-time fame rates are desired in these scanning radar 3D imaging systems to achieve high throughput. High throughput can also be achieved at lower near real-time frame rates if the 3D images can be motion compensated before detection of concealed objects. In this paper we present a method for motion compensation of submillimeter wave 3D imaging radar data used for Security Screening. We demonstrate the method on simulated 3D radar data with known motion errors with good results.
Jun Zhuang - One of the best experts on this subject based on the ideXlab platform.
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Security Screening queues with impatient applicants a new model with a case study
European Journal of Operational Research, 2018Co-Authors: Ali Pala, Jun ZhuangAbstract:Security Screening policies are critical in military contexts, airports, ports, and visa issuance processes in order to minimize risks from terrorists, smugglers, fugitives, and others. However, such Screening procedures also increase congestion and inconvenience for normal applicants, which may create a trade-off for the authorities balancing between risk and congestion. In this paper, we develop a game-theoretical model to investigate optimal Screening policies that acknowledges the trade-offs between risk, congestion, and abandonment behavior of the applicants. To calculate the average waiting time in the Screening queue with heterogeneous impatient applicants, we use a two-dimensional Markov chain model. As a case study, we tackle the Security Screening process of US visa applications by conducting an online survey. Collected data shows some key aspects of applicant preferences such as abandonment behavior. We conduct sensitivity analysis for our model. We show that if the authorities take the abandonment behavior of the applicants into account beforehand, they may achieve higher utility depending on the characteristics of applicants.
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two stage Security Screening strategies in the face of strategic applicants congestions and Screening errors
Annals of Operations Research, 2017Co-Authors: Cen Song, Jun ZhuangAbstract:In a Security Screening system, a tighter Screening policy not only increases the Security level, but also causes congestion for normal people, which may deter their use and decrease the approver’s payoff. Adapting to the Screening policies, adversary and normal applicants choose whether to enter the Screening system. Security managers could use Screening policies to deter adversary applicants, but could also lose the benefits of admitting normal applicants when they are deterred, which generates a tradeoff. This paper analyzes the optimal Screening policies in an imperfect two-stage Screening system with potential Screening errors at each stage, balancing Security and congestion in the face of strategic normal and adversary applicants. We provide the optimal levels of Screening strategies for the approver and the best-response application strategies for each type of applicant. This paper integrates game theory and queueing theory to study the optimal two-stage policies under discriminatory and non-discriminatory Screening policies. We extend the basic model to the optimal allocation of total service rate to the assumed two types of applicants at the second stage and find that most of the total service rate are assigned to the service rate for the assumed “Bad” applicants. This paper provides some novel policy insights which may be useful for Security Screening practices.
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N-stage Security Screening strategies in the face of strategic applicants
Reliability Engineering & System Safety, 2017Co-Authors: Cen Song, Jun ZhuangAbstract:From one perspective, tighter Security Screening has the benefit of deterring adversary passengers and enhancing safety. However, this approach can also produce congestion problems for normal passengers. Adapting to Screening policies, both adversary and normal passengers decide their application strategies to the Security system to maximize their payoffs, which in turn affects the Security agent's payoff. This paper integrates game theory and queueing theory to analyze an N-stage imperfect Screening model that considers reject or pass decisions, in which applicants have the chance to be passed or rejected at each stage of the system. An imperfect three-stage Screening model is numerically illustrated. Furthermore, the application probabilities, Screening probabilities and approver's payoff as functions of the number of Screening stages are analyzed. This paper provides some novel insights on Screening policies and the optimal number of Screening stages which would help Security Screening policy makers.
James A. Levine - One of the best experts on this subject based on the ideXlab platform.
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Seeing through the face of deception: Thermal imaging offers a promising hands-off approach to mass Security Screening
Nature, 2002Co-Authors: Ioannis Pavlidis, Norman L Eberhardt, James A. LevineAbstract:Thermal imaging offers a promising hands-off approach to mass Security Screening. We have developed a high-definition thermal-imaging technique that can detect attempted deceit by recording the thermal patterns from people's faces. This technique has an accuracy comparable to that of polygraph examination by experts and has potential for application in remote and rapid Security Screening, without the need for skilled staff or physical contact.
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Seeing through the face of deception: Thermal imaging offers a promising hands-off approach to mass Security Screening
Nature, 2002Co-Authors: Ioannis Pavlidis, Norman L Eberhardt, James A. LevineAbstract:Thermal imaging offers a promising hands-off approach to mass Security Screening.
Toby P. Breckon - One of the best experts on this subject based on the ideXlab platform.
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ICDP - On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening
7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), 2020Co-Authors: Mikolaj E. Kundegorski, Andre Mouton, Samet Akcay, Michael Devereux, Toby P. BreckonAbstract:Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage Security X-ray imagery. Using a classical BoVW model with a range of feature point detectors and descriptors, supported by both Support Vector Machine (SVM) and Random Forest classification, we illustrate the current performance capability of approaches following this image classification paradigm over a large X-ray baggage imagery data set. An optimal statistical accuracy of 0.94 (true positive: 83%; false positive: 3.3%) is achieved using a FAST-SURF feature detector and descriptor combination for a firearms detection task. Our results indicate comparative levels of performance for BoVW based approaches for this task over extensive variations in feature detector, feature descriptor, vocabulary size and final classification approach. We further demonstrate a by-product of such approaches in using feature point density as a simple measure of image complexity available as an integral part of the overall classification pipeline. The performance achieved characterises the potential for BoVW based approaches for threat object detection within the future automation of X-ray Security Screening against other contemporary approaches in the field.
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on the evaluation of prohibited item classification and detection in volumetric 3d computed tomography baggage Security Screening imagery
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Qian Wang, Neelanjan Bhowmik, Toby P. BreckonAbstract:X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation Security Screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of extending the automatic prohibited item detection from 2D X-ray imagery to volumetric 3D CT baggage Security Screening imagery. To these ends, we take advantage of 3D Convolutional Neural Neworks (CNN) and popular object detection frameworks such as RetinaNet and Faster R-CNN in our work. As the first attempt to use 3D CNN for volumetric 3D CT baggage Security Screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features. Subsequently, we evaluate object detection performance of different architectures on volumetric 3D CT baggage images. The results of our experiments on Bottle and Handgun datasets demonstrate that 3D CNN models can achieve comparable performance (98% true positive rate and 1.5% false positive rate) to traditional methods but require significantly less time for inference (0.014s per volume). Furthermore, the extended 3D object detection models achieve promising performance in detecting prohibited items within volumetric 3D CT baggage imagery with 76% mAP for bottles and 88% mAP for handguns, which shows both the challenge and promise of such threat detection within 3D CT X-ray Security imagery.
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On using feature descriptors as visual words for object detection within X-ray baggage Security Screening
7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), 2016Co-Authors: Mikolaj E. Kundegorski, Andre Mouton, Samet Akcay, Michael Devereux, Toby P. BreckonAbstract:Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage Security X-ray imagery. Using a classical BoVW model with a range of feature point detectors and descriptors, supported by both Support Vector Machine (SVM) and Random Forest classification, we illustrate the current performance capability of approaches following this image classification paradigm over a large X-ray baggage imagery data set. An optimal statistical accuracy of 0.94 (true positive: 83%; false positive: 3.3%) is achieved using a FAST-SURF feature detector and descriptor combination for a firearms detection task. Our results indicate comparative levels of performance for BoVW based approaches for this task over extensive variations in feature detector, feature descriptor, vocabulary size and final classification approach. We further demonstrate a by-product of such approaches in using feature point density as a simple measure of image complexity available as an integral part of the overall classification pipeline. The performance achieved characterises the potential for BoVW based approaches for threat object detection within the future automation of X-ray Security Screening against other contemporary approaches in the field.
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materials based 3d segmentation of unknown objects from dual energy computed tomography imagery in baggage Security Screening
Pattern Recognition, 2015Co-Authors: Andre Mouton, Toby P. BreckonAbstract:We present a novel technique for the 3D segmentation of unknown objects from cluttered dual-energy Computed Tomography (CT) data obtained in the baggage Security-Screening domain. Initial materials-based coarse segmentations, generated using the Dual-Energy Index (DEI), are refined by partitioning at automatically detected regions. Partitioning is guided by a novel random forest based quality metric, trained to recognise high-quality, single-object segments. A second novel segmentation quality measure is presented for quantifying the quality of full segmentations based on the random forest metric of the constituent parts and the error in the number of objects segmented. In a comparative evaluation between the proposed approach and three state-of-the-art volumetric segmentation techniques designed for single-energy CT data (two region-growing 1,2] and one graph-based 3]) our method is shown to outperform both region-growing methods in terms of segmentation quality and speed. Although the graph-based approach generates more accurate partitions, it is characterised by high processing times and is significantly outperformed by the proposed method in this regard. The observations made in this study indicate that the proposed segmentation technique is well-suited to the baggage Security-Screening domain, where the demand for computational efficiency is paramount to maximise throughput. HighlightsNovel dual-energy materials-based 3D segmentation technique.Two novel random forest-based segmentation quality metrics.Novel segmentation refinement procedure.Comparative evaluation using dual-energy baggage Screening CT data.Demonstrate high-quality segmentations at low processing times.
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A review of automated image understanding within 3D baggage computed tomography Security Screening
Journal of X-Ray Science and Technology, 2015Co-Authors: Andre Mouton, Toby P. BreckonAbstract:Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport Security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent Security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for baggage Security Screening CT.
