Scale robust IMU-assisted KLT for stereo visual odometry solution

We propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context

B-HoD: A Lightweight and Fast Binary Descriptor for 3D Object Recognition and Registration

3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance

Stixel Based Scene Understanding for Autonomous Vehicles

We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors

Understanding Law Enforcement Intelligence Processes: Report to the Office of University Programs, Science and Technology Directorate

Despite clear evidence of significant changes, very little research exists that examines issues related to the intelligence practices of state, local, and tribal (SLT) law enforcement agencies. Important questions on the nature of the issues that impact SLT intelligence practices remain. While there is some uncertainty among SLT law enforcement about current terrorism threats, there is certainty that these threats evolve in a largely unpredictable pattern. As a result there is an ongoing need for consistent and effective information collection, analysis and sharing. Little information is known about perceptions of how information is being shared between agencies and whether technologies have improved or hurt information sharing, and little is known about whether agencies think they are currently prepared for a terrorist attack, and the key factors distinguishing those that think they are compared to those who do not. This study was designed to address these issues, and a better understanding of these issues could significantly enhance intelligence practices and enhance public safety.This research was supported by the Department of Homeland Science and Technology Directorate’s Office of University Programs through Award Number 2012-ST-061-CS0001, Center for the Study of Terrorism and Behavior (CSTAB) 2.13 made to START to investigate the understanding and countering of terrorism within the U.S. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security or START

Understanding the Intelligence Practices of State, Local, and Tribal Law Enforcement Agencies

In addition, the study examined the activities of three fusion centers in order to identify strategies that are successful in increasing the information flow across agencies, the major obstacles to effective intelligence-gathering and information-sharing, and identify key practices for integrating domestic intelligence into the information-sharing environment and overcoming these obstacles. The study found that although significant progress has been made since 9/11 in installing fundamental policy and procedures related to building the intelligence capacity of law enforcement, there is significant room for improvement and a need to move agencies forward to be consistent with key requirements. Also, fusion centers are further along in instituting intelligence policies and practices than are individual law enforcement agencies. This is most likely because there has been a focus on developing fusion center operations and expertise by both the Department of Homeland Security and the Department of Justice. In addition, both samples of respondents emphasized that they have worked at building relationships with a diverse range of agencies, but they also indicated that they are not completely satisfied with these relationships. Further, there is a significant amount of information coming into and going out of these agencies. It is likely that without sufficient analysts within the organizations or poorly trained analysts, there are missed opportunities for strategic and tactical understanding of homeland security and criminal threats. Assessing the performance of analysts is difficult, but respondents emphasized the need to focus on the quality of strategic and tactical products produced

Law Enforcement’s Information Sharing Infrastructure: A National Assessment

The September 11 attacks impacted society generally, and law enforcement specifically, in dramatic ways. One of the major trends has been changing expectations regarding criminal intelligence practices among state, local, and tribal (SLT) law enforcement agencies and the need to coordinate intelligence efforts and share information at all levels of government. In fact, enhancing intelligence efforts has emerged as a critical issue for the prevention of all threats and crimes. To date, an increasing number of SLT law enforcement agencies have expanded their intelligence capacity, and there have been fundamental changes in the national, state, and local information sharing infrastructure. Moreover, critical to these expanding information sharing expectations is the institutionalization of fusion centers (FCs). Despite these dramatic changes, an expanding role, and the acknowledgement that local law enforcement intelligence is critical to the prevention and deterrence of threats and crimes, very little research exists that highlights issues related to the intelligence practices of SLT law enforcement agencies and FCs.1 This research describes what agencies are doing to build an intelligence capacity and assesses the state of information sharing among agencies. Specifically, a national survey was developed to examine the experiences of SLT agencies and FCs for building an intelligence capacity as well as to understand critical gaps in the sharing of information regarding intelligence

Fluorescence resonance energy transfer sensors for quantitative monitoring of pentose and disaccharide accumulation in bacteria

<p>Abstract</p> <p>Background</p> <p>Engineering microorganisms to improve metabolite flux requires detailed knowledge of the concentrations and flux rates of metabolites and metabolic intermediates <it>in vivo</it>. Fluorescence resonance energy transfer sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. These sensors have been applied successfully in mammalian and plant cells but potentially could also be used to monitor steady-state levels of metabolites in microorganisms using fluorimetric assays. Sensors for hexose and pentose carbohydrates could help in the development of fermentative microorganisms, for example, for biofuels applications. Arabinose is one of the carbohydrates to be monitored during biofuels production from lignocellulose, while maltose is an important degradation product of starch that is relevant for starch-derived biofuels production.</p> <p>Results</p> <p>An <it>Escherichia coli </it>expression vector compatible with phage λ recombination technology was constructed to facilitate sensor construction and was used to generate a novel fluorescence resonance energy transfer sensor for arabinose. In parallel, a strategy for improving the sensor signal was applied to construct an improved maltose sensor. Both sensors were expressed in the cytosol of <it>E. coli </it>and sugar accumulation was monitored using a simple fluorimetric assay of <it>E. coli </it>cultures in microtiter plates. In the case of both nanosensors, the addition of the respective ligand led to concentration-dependent fluorescence resonance energy transfer responses allowing quantitative analysis of the intracellular sugar levels at given extracellular supply levels as well as accumulation rates.</p> <p>Conclusion</p> <p>The nanosensor destination vector combined with the optimization strategy for sensor responses should help to accelerate the development of metabolite sensors. The new carbohydrate fluorescence resonance energy transfer sensors can be used for <it>in vivo </it>monitoring of sugar levels in prokaryotes, demonstrating the potential of such sensors as reporter tools in the development of metabolically engineered microbial strains or for real-time monitoring of intracellular metabolite during fermentation.</p

Local feature based automatic target recognition for future 3D active homing seeker missiles

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes

IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction

An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS

DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection

Recent studies have proposed that traditional security technology – involving pattern-matching algorithms that check predefined pattern sets of intrusion signatures – should be replaced with sophisticated adaptive approaches that combine machine learning and behavioural analytics. However, machine learning is performance driven, and the high computational cost is incompatible with the limited computing power, memory capacity and energy resources of portable IoT-enabled devices. The convoluted nature of deep-structured machine learning means that such models also lack transparency and interpretability. The knowledge obtained by interpretable learners is critical in security software design. We therefore propose two novel models featuring a common Deep Extraction and Mutual Information Selection (DEMISe) element which extracts features using a deep-structured stacked autoencoder, prior to feature selection based on the amount of mutual information (MI) shared between each feature and the class label. An entropy-based tree wrapper is used to optimise the feature subsets identified by the DEMISe element, yielding the DEMISe with Tree Evaluation and Regression Detection (DETEReD) model. This affords ‘white box’ insight, and achieves a time to build of 603 seconds, a 99.07% detection rate, and 98.04% model accuracy. When tested against AWID, the best-referenced intrusion detection dataset, the new models achieved a test error comparable to or better than state-of-the-art machine-learning models, with a lower computational cost and higher levels of transparency and interpretability