Civilian Surveillance Research Articles

Thermal Imaging and Clandestine Surveillance using Low‐Cost Polymers with Long‐Wave Infrared Transparency

AbstractWith increasing use of infrared imaging in medical diagnostics, military and civilian surveillance, and navigation of autonomous vehicles, there is a need for low‐cost alternatives to traditional materials used in infrared optics such as germanium. Sulfur‐rich copolymers hold promise, as they are made from low‐cost feedstocks and have a high refractive index. In this report, cyclopentadiene is copolymerized with sulfur to provide a plastic with the highest long‐wave infrared transparency reported to date for this class of materials. Diverse lens architectures are accessible through melt casting or reactive injection molding. The featured copolymer is black, which enabled its use as an infrared‐transparent blind for protection of thermal imaging equipment and clandestine surveillance. These findings portend expanded use of sulfur copolymers in infrared optics.

Civilian Surveillance in the War in Ukraine: Mobilizing the Agency of the Observers of War

The war in Ukraine sees local and foreign civilians play active roles in the conflict, mainly through the participatory gathering and sharing of intelligence and open-source investigations of alleged human rights violations and war crimes. These surveillance practices seen in the war in Ukraine are not novel. Vigilantism campaigns have normalized since the War on Terror, while open-source information is increasingly recognized as a legitimate tool for human rights and international criminal justice investigation. Yet, their importance in the war in Ukraine highlights the agentic power of civilian surveillance. The proliferation of digital technologies empowers civilians to become inevitable actors in all spheres of politics, including war. However, across these practices, I argue that the Ukrainian government and its Western allies harness this agency as operational and narrative weapons. Patriotism and morality are pushed forward to mobilize individuals to participate in the war despite the risks that vigilantes and open-source investigators have to assume: risks of retaliation by Russian forces and lost independence.

Civilian health surveillance, population studies and participation

RESUMO Em meados dos anos 1990, Victor Valla propôs a incorporação da participação da população à prática da vigilância em saúde, por meio da educação popular de Paulo Freire. Esse contraponto às práticas tradicionais da vigilância, nomeadas vigilância civil da saúde, somam-se à concepção ampliada de saúde, e possuem forte ligação com a perspectiva crítica da epidemiologia enquanto meio de compreender a relação dialética entre classes sociais e seus espaços vividos. A prática da vigilância civil pretende superar lacunas importantes deixadas pelos métodos tradicionais de investigação em saúde pública, como falta de atenção aos contextos socioculturais, construção do risco localizada somente no indivíduo e representação de agendas de saúde pública que privilegiam e patologizam certos comportamentos. Nesse sentido, o presente trabalho debate o conceito de vigilância civil da saúde, o locus de discussão dos estudos de população na reificação do papel do efeito contextual para a explicação da produção social da saúde e a incorporação da participação popular à vigilância em saúde como elemento de transformação social. O aprofundamento dessa discussão, no limite, permite uma construção participativa de novos modelos de saúde concentrados na redução efetiva das iniquidades em saúde e, consequentemente, universalização efetiva do direito à saúde.

Mining regular behaviors based on spatiotemporal trajectory multi‐dimensional features

In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi-dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi-dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi-dimensional features and density clustering is proposed. Firstly, multi-dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

Three-dimensional route planning for multiple unmanned aerial vehicles using Salp Swarm Algorithm

ABSTRACT Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimisation problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035%, respectively, when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even in commercial services like package delivery by drones.

Study of subjective and objective quality assessment of infrared compressed images

Abstract Given the lack of accessible infrared compressed images’ benchmarks annotated by human subjects, this work presents a new database with the aim of studying both subjective and objective image quality assessment (IQA) on compressed long wavelength infrared (LWIR) images. The database contains 20 reference (pristine) images and 200 distorted (degraded) images obtained by application of the most known compression algorithms used in multimedia and communication fields, namely: JPEG and JPEG-2000. Each compressed image is evaluated by 31 subjects having different levels of experience in LWIR images. Mean opinion scores (MOS) and natural scene statistics (NSS) of pristine and compressed images are elaborated to study the performance of the database. Five analyses are conducted on collected images and subjective scores, namely: analysis by compression type, analysis by file size, analysis by reference image, analysis by quality level and analysis by subject. Moreover, a wide set of objective IQA metrics is applied on the images and the obtained scores are compared with the collected subjective scores. Results show that objective IQA measures correlate with human subjective results with a degree of agreement up to 95 %, so this benchmark is promising to improve existing and develop new IQA measures for compressed LWIR images. Thanks to a real-world surveillance original images based on which we analyze how image compression and quality level affect the quality of compressed images, this database is primarily suitable for (military and civilian) surveillance applications. The database is accessible via the link: https://github.com/azedomar/compressed-LWIR-images-IQA-database. As a follow-up to this work, an extension of the database is underway to study other types of distortion in addition to compression.

Learning Resource Allocation in Active-Passive Radar Sensor Networks

Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.

Improving Accuracy of Source Localization Algorithms Using Kalman Filter Estimator

Passive geolocation of emitters provides benefits to military and civilian surveillance and in the field of aerospace. Passive geolocation can be done by exploiting some of the parameters of the signal transmitted by the source or emitter. In this paper the Time Difference Of Arrival (TDOA) of the signal at different receivers is used to estimate the position of the emitter. This paper aims at presenting the research for the development of an algorithm which can be used in a distributed network of acoustic sensors consisting of an emitter and five stationary receivers. By means of measurement of time difference of arrival of underwater acoustic signal the source position is estimated using three different algorithms. Taylor series, closed form and two stage weighted least square methods are used to estimate the stationary source location by TDOAmeasurements of received signal and compared on the basis of Cramer Rao Lower Bound (CRLB). Comparison of performance between these methods are done and analysed.Use of a kalman filter is proposed to improve the accuracy of all the three methods and the results are compared and analysed.

Infrastructural obfuscation: unpacking the carceral logics of the Ring surveillant assemblage

ABSTRACT Amazon’s Ring relies on infrastructural obfuscation to hide their infrastructures through urban camouflage (as doorbells, floodlights, sensors) while simultaneously expanding the carceral state and extending the industrial police-surveillant state. Through a critical analysis of the Ring surveillant assemblage, this paper reveals the way Ring and its associated apps produce fear and paranoia of the racialized Other, promote community buy-in that more surveillance will improve safety, while obscuring private partnerships with local law enforcement. I argue Ring cannot be viewed in isolation from its entangled corporate owner, Amazon, which relies on an ever-expanding infrastructural network, including surveillance of their highly prized package delivery service. Ring is more than individual community members installing doorbells with cameras that extend beyond their property into public space; it is the blurring of boundaries between police work and civilian surveillance, the reliance on obscured digital infrastructures that hide to whom and what users are connecting when they install a Ring device, and the expansion of Amazon’s vast infrastructural power. By recentering critique on the infrastructural backbone, made up of discourses and fixtures, this paper argues for the need for infrastructural accountability from companies such as Amazon, who not only profit from fear, but actively reproduce structural violence through their data infrastructures.

T-RexNet-A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects.

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.

A smart lightweight privacy preservation scheme for IoT-based UAV communication systems

Unmanned Aerial Vehicle (UAV) has extensively been practiced in the military and civilian surveillance systems that access sensitive data over the cellular networks. However, channel insecurity and battery limitation may not protect the aerial coverage area. Thus, sensitive information gathered through aerial vehicles causes security threats. To manage the security issues, Smart Internet of Drone (S-IoD) have been evolved to use Intelligent Personal Assistant (IPA) as a software agent while monitoring and observing areas of interest. The current state-of-the-art technologies provide ubiquitous communication to enable several Internet of Things (IoT) paradigms. It achieves a feature of a decision support system that allows the smart interaction and communication between real-time entities. IPA offers smart interaction with other smart real-time entities to gain the user’s knowledge and awareness. This paper presents an S-IoD framework for a UAV environment that independently collects sensible information. In order to reduce the computation cost of the authentication protocol, a lightweight privacy-preserving scheme (L-PPS) is introduced. The proposed L-PPS is constructive to provide the robustness between the IoT devices with a valid authentication period. To demonstrate the security and performance efficiencies, the formal verification was performed using a verification tool, Scyther, and a random oracle model. In addition, the proposed L-PPS introduces a secret token and dynamic user authentication to speed up the authentication process between the communication entities. Importantly, the authentication session of L-PPS does not use any complex cryptographic operations, whereby it has less computation and communication costs to meet the standard constraints of surveillance systems. Moreover, the obtained simulation analysis proves that the proposed L-PPS achieves better quality metrics than other authentication schemes in the literature.

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.

Random forest‐based track initiation method

In this study, a novel method based on the random forest is presented to solve the problem of track initiation in the air-traffic-control (ATC) radar system. ATC radar is the most common civilian surveillance radar. There are dense targets with different moving models in its observation area. When implementing track initiation, previous heuristic methods often fail to meet its high requirements for target detection rate and false track suppression. In the method proposed in this study, the general track initiation problem is modelled as a two-category classification problem of measurement sequences. From the historical data, the sample set is constructed. The classification function is derived by Breiman's random forest. Simulation experiments and measurement experiments show that the proposed method outperforms conventional heuristic methods in the scene of the ATC system.

Streamlined Development of Analytic Fusion Capability for Health Surveillance

Streamlined Development of Analytic Fusion Capability for Health Surveillance

Focus-before-detection radar signal processing: part i—challenges and methods

During the World War II (WWII), radar was invented as an all-day, all-weather, long-range sensor. Over the past 75 years or so, radar has acted as the clairvoyance and clairaudience of humans and has had wide applications in both defense and civilian fields, e.g., surveillance, reconnaissance, fire control, border monitoring, collision avoidance, and traffic control. Along with radar development, radar signal processing (RSP) always plays an important role. In 1943, North pointed out that the matched filter should be the foundation of radar to detect targets from echoes generated by the known transmitting waveform. In 1950, Woodward and Davies introduced the Bayesian statistics theory into the RSP field and proposed the constant false alarm ratio (CFAR) rule for target detection. Based on matched filtering and the CFAR rule, the embryonic framework of classic RSP has been formed and used thus far. In 1953, Woodward further proposed an ambiguity function as a useful tool to evaluate the range and Doppler discrimination abilities of a transmitting waveform, which paves the foundation for radar waveform design. Furthermore, to deal with the tradeoff between large transmitting energy and high range resolution, the early unmodulated rectangular waveform has been replaced by more waveforms with a large time-bandwidth product (TBP), such as linear frequency modulation (LFM) and phase-coded waveforms. These waveforms with large TBP have been widely used to realize the range compression via matched filter.

Sense and Avoid Airborne Radar Implementations on a Low-Cost Weather Radar Platform

Traditionally, multi-mission applications in airborne radar are implemented through very expensive phased array architectures. The emerging applications from civilian surveillance, on the other hand, prefer low-cost and low-SWaP (space, weight and power) systems. This study introduces asoftware-basedsolutionthatintendstouselow-costhardwareandadvancedalgorithms/processing backend to meet the remote sensing goals for multi-mission applications. The low-cost airborne radar platform from Garmin International is used as a representative example of the system platform. The focus of this study is the optimal operating mode, data quality and algorithm development in cases of all-weather sense and avoid (SAA) applications. The main challenges for the solution are the resolution limitation due to the small aperture size, limitations from the field-of-view (FOV) and the scan speed from mechanical scanning. We show that the basic operational needs can be satisfied with software processing through various algorithms. The concept and progress of polarimetric airborne radar for dual-function operations at X-band Generation 1 (PARADOX1) based on the platform are also discussed.

Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking

In the event of natural or man-made disasters, providing rapid situational awareness through video/image data collected at salient incident scenes is often critical to the first responders. However, computer vision techniques that can process the media-rich and data-intensive content obtained from civilian smartphones or surveillance cameras require large amounts of computational resources or ancillary data sources that may not be available at the geographical location of the incident. In this paper, we propose an incident-supporting visual cloud computing solution by defining a collection, computation, and consumption (3C) architecture supporting fog computing at the network edge close to the collection/consumption sites, which is coupled with cloud offloading to a core computation, utilizing software-defined networking (SDN). We evaluate our 3C architecture and algorithms using realistic virtual environment test beds. We also describe our insights in preparing the cloud provisioning and thin-client desktop fogs to handle the elasticity and user mobility demands in a theater-scale application. In addition, we demonstrate the use of SDN for on-demand compute offload with congestion-avoiding traffic steering to enhance remote user quality of experience in a regional-scale application. The optimization between fogs computing at the network edge with core cloud computing for managing visual analytics reduces latency, congestion, and increases throughput.

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods.

The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4–0.7μm), near-infrared (NIR, 0.7–1.0μm) and long-wave infrared (LWIR, 8–14μm) motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian) people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation) all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer). The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false) color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic) image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs can be used to define color mappings that give the multi-band imagery a realistic color appearance.

Design and Manufacture of a Solar-Powered Unmanned Aerial Vehicle for Civilian Surveillance Missions

In order to promote the development of renewable energy and take advantage of the new technologies for the benefit of sustainability, both the design and the manufacture methodologies of an experimental solarpowered unmanned aerial vehicle for civilian surveillance applications are presented. Throughout the document, it is provided the historical process around the development of the aircraft. Therefore, in the first part, it is shown the aerodynamic design, which includes the 2-D and 3-D analyses of the wing platform using numerical and experimental methods, the analytical design of the empennage configuration, and the main characteristics of the performance analysis. In addition, major systems and components that characterize the aircraft are described, such as the photovoltaic solar cells configuration as well as the electronics and control system into the unmanned aerial vehicle. Lastly, the modeling for the weights distribution of the components was carried out in a preliminary test using CAD tools. Thus, it was obtained a suitable process for the manufacture of the unmanned aerial vehicle, considering that the purpose of the aircraft is to be as light and aerodynamic as possible to accomplish the mission for which it was created.

Design and Fabrication of Voice Controlled Unmanned Aerial Vehicle

<p class="Default">Unmanned Aerial Vehicles have gained well known attention in recent years for a numerous applications such as military, civilian surveillance operations as well as search and rescue missions. The UAVs are not controlled by professional pilots and users have less aviation experience. Therefore it seems to be purposeful to simplify the process of aircraft controlling. The objective is to design, fabricate and implement an unmanned aerial vehicle which is controlled by means of voice recognition. In the proposed system, voice commands are given to the quadcopter to control it autonomously. This system is navigated by the voice input. The control system responds to the voice input by voice recognition process and corresponding algorithms make the motors to run at specified speeds which controls the direction of the quadcopter.</p>