Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect’s body despite concealment

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by the initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot post-processing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and collected dataset. Additionally, the influence of different input formats, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.Comment: 12 page

Multisensor Concealed Weapon Detection Using the Image Fusion Approach

Detection of concealed weapons is an increasingly important problem for both military and police since global terrorism and crime have grown as threats over the years. This work presents two image fusion algorithms, one at pixel level and another at feature level, for efficient concealed weapon detection application. Both the algorithms presented in this work are based on the double-density dual-tree complex wavelet transform (DDDTCWT). In the pixel level fusion scheme, the fusion of low frequency band coefficients is determined by the local contrast, while the high frequency band fusion rule is developed with consideration of both texture feature of the human visual system (HVS) and local energy basis. In the feature level fusion algorithm, features are exacted using Gaussian Mixture model (GMM) based multiscale segmentation approach and the fusion rules are developed based on region activity measurement. Experiment results demonstrate the robustness and efficiency of the proposed algorithms

Combining Top-down and Bottom-up Visual Saliency for Firearms Localization

Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people\u2019s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

Investigation of late time response analysis for security applications

The risk of armed attack by individual’s intent on causing mass casualties against soft targets, such as transport hubs continues. This has led to an increased need for a robust, reliable and accurate detection system for concealed threat items. This new system will need to improve upon existing detection systems including portal based scanners, x-ray scanners and hand held metal detectors as these all suffer from drawbacks of limited detection range and relatively long scanning times. A literature appraisal has been completed to assess the work being undertaken in the relevant field of Concealed Threat Detection (CTD). From this Ultra-Wide Band (UWB) radar has been selected as the most promising technology available for CTD at the present. UWB radar is provided by using Frequency Modulated Continuous Waves (FMCW) from laboratory test equipment over a multi gigahertz bandwidth. This gives the UWB radar the ability to detect both metallic and dielectric objects. Current published results have shown that it is possible to use the LTR technique to detect and discriminate both single objects isolated in air and multiple objects present within the same environment. A Vector Network Analyser (VNA) has been used to provide the Ultra-Wide Band (UWB) Frequency Modulated Continuous Wave (FMCW) radar signal required for the LTR technique. This thesis presents the application of the Generalized Pencil-of-Function (GPOF), Dual Tree Wavelet Transform (DTWT) and the Continuous Wavelet Transform (CWT), both real and complex valued, in Late Time Response (LTR) security analysis to produce a viable detection algorithm. Supervised and unsupervised Artificial Neural Networks (ANN) have been applied to develop a successful classification scheme for Concealed Threat Detection (CTD) in on body security screening. Signal deconvolution and other techniques have been applied in post processing to allow for extraction of the LTR signal from the scattered return. Data vectorization has been applied to the extracted LTR signal using an unsupervised learning based ANN to prepare data for classification. Classification results for both binary threat/non-threat classifiers and a group classifier are presented. The GPOF method presented true positive classification results approaching 72% with wavelet based methods offering between 98% and 100%

Explosive Detection Equipment and Technology for Border Security

This report contains a brief survey of Explosives Detection Technology,as it is applied for inspection of goods and passengers at borders, and explains the role of European legislation and the European Commission¿s research programs in this field. It describes the techniques of trace and bulk explosives detection that are in use, the latest techniques that are in development and the characteristics of explosives that are, or might be, used to provide a signature for exploitation in detection technology. References to academic reviews are included for those wishing to study the subject in greater depth. Some additional details are given concerning plastic and liquid explosives, which are a threat of particular current importance. The report also contains a brief account of relevant European trade, safety and security legislation, a description of recent policy initiatives and tables of related European Commission funded research projects. Contact details of commercial companies selling explosive detection products are also provided.JRC.G.6-Sensors, radar technologies and cybersecurit