Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios.Comment: 20 pages, IoTD

A Photogrammetry-Based Hybrid System for Dynamic Tracking and Measurement

Noncontact measurements of lightweight flexible aerospace structures present several challenges. Objects are usually mounted on a test stand because current noncontact measurement techniques require that the net motion of the object be zero. However, it is often desirable to take measurements of the object under operational conditions, and in the case of miniature aerial vehicles (MAVs) and deploying space structures, the test article will undergo significant translational motion. This thesis describes a hybrid noncontact measurement system which will enable measurement of structural kinematics of an object freely moving about a volume. By using a real-time videogrammetry system, a set of pan-tilt-zoom (PTZ) cameras is coordinated to track large-scale net motion and produce high-speed, high-quality images for photogrammetric surface reconstruction. The design of the system is presented in detail. A method of generating the calibration parameters for the PTZ cameras is presented and evaluated and is shown to produce good results. The results of camera synchronization tests and tracking accuracy evaluation are presented as well. Finally, a demonstration of the hybrid system is presented in which all four PTZ cameras track an MAV in flight

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders

QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios

The concerns about individuals security have justified the increasing number of surveillance cameras deployed both in private and public spaces. However, contrary to popular belief, these devices are in most cases used solely for recording, instead of feeding intelligent analysis processes capable of extracting information about the observed individuals. Thus, even though video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant details about the subjects that took part in a crime depends on the manual inspection of recordings. As such, the current goal of the research community is the development of automated surveillance systems capable of monitoring and identifying subjects in surveillance scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at designing a visual surveillance system capable of acquiring biometric data at a distance (e.g., face, iris or gait) without requiring human intervention in the process, as well as devising biometric recognition methods robust to the degradation factors resulting from the unconstrained acquisition process. Regarding the first goal, the analysis of the data acquired by typical surveillance systems shows that large acquisition distances significantly decrease the resolution of biometric samples, and thus their discriminability is not sufficient for recognition purposes. In the literature, diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring high-resolution imagery at a distance, particularly when using a master-slave configuration. In the master-slave configuration, the video acquired by a typical surveillance camera is analyzed for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged at high-resolution by the PTZ camera. Several methods have already shown that this configuration can be used for acquiring biometric data at a distance. Nevertheless, these methods failed at providing effective solutions to the typical challenges of this strategy, restraining its use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development of a biometric data acquisition system based on the cooperation of a PTZ camera with a typical surveillance camera. The first proposal is a camera calibration method capable of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ camera. The second proposal is a camera scheduling method for determining - in real-time - the sequence of acquisitions that maximizes the number of different targets obtained, while minimizing the cumulative transition time. In order to achieve the first goal of this thesis, both methods were combined with state-of-the-art approaches of the human monitoring field to develop a fully automated surveillance capable of acquiring biometric data at a distance and without human cooperation, designated as QUIS-CAMPI system. The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis of the performance of the state-of-the-art biometric recognition approaches shows that these approaches attain almost ideal recognition rates in unconstrained data. However, this performance is incongruous with the recognition rates observed in surveillance scenarios. Taking into account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a distance ranging from 5 to 40 meters and without human intervention in the acquisition process. This set allows to objectively assess the performance of state-of-the-art biometric recognition methods in data that truly encompass the covariates of surveillance scenarios. As such, this set was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained by the nine methods specially designed for this competition. In addition, the data acquired by the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the development of methods robust to the covariates of surveillance scenarios. The first proposal regards a method for detecting corrupted features in biometric signatures inferred by a redundancy analysis algorithm. The second proposal is a caricature-based face recognition approach capable of enhancing the recognition performance by automatically generating a caricature from a 2D photo. The experimental evaluation of these methods shows that both approaches contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivíduos tem justificado o crescimento do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos. Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de monitorizar e identificar indivíduos em ambientes de vídeo-vigilância. Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se 1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias (e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas. No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis. Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave. Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse (e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo, esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações que maximiza o número de diferentes sujeitos observados e simultaneamente minimiza o tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os dois métodos propostos foram combinados com os avanços alcançados na área da monitorização de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação dos indivíduos no processo, designado por sistema QUIS-CAMPI. O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais, esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos capturados num ambiente real de vídeo-vigilância. Como tal, este conjunto foi utilizado para promover a primeira competição de reconhecimento biométrico em ambientes não controlados. Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9 métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para aumentar a robustez aos fatores de degradação observados em ambientes de vídeo-vigilância. O primeiro é um método para detetar características corruptas em assinaturas biométricas através da análise da redundância entre subconjuntos de características. O segundo é um método de reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir as taxas de erro em dados adquiridos de forma não controlada