Various types of small mobile objects such as recreational unmanned vehicles have become easily approachable devices to the public because of technology advancements. The technology advancements make it possible to manufacture small, light, and easy to control unmanned vehicles, therefore the public are able to handily access those unmanned vehicles. As the accessibility to unmanned vehicles for recreational purposes, accidents or attacks to threat a person using those the unmanned vehicles have been arising and growing rapidly. A specific person could be a target of a threat using an unmanned vehicle in open public places due to its small volume and mobility. Even though an unmanned vehicle approaches to a person, it could be difficult to detect the unmanned vehicle before the person encounters because of the compact size and maneuverability. <div><br></div><div>This research is to develop a radar system that is able to operate in open public areas to detect and track unmanned vehicles. It is not capable using existing radar systems such as for navigation, aviation, national defense, air traffic control, or weather forecasting to monitor and scan public places because of large volume, high operation cost, and danger to human health of the radar systems. For example, if electromagnetic fields emitted from high-power radar penetrate exposed skin surface or eyes, the energy from the electromagnetic fields can cause skin burns, eye cataracts, or more (Zamanian & Hardiman, 2005). Therefore, a radar system that can perform at the public place is necessary for monitoring and surveillance the area. <div><br></div><div>The hardware of this proposed radar system is composed of three parts: 1) radio frequency transmission and receiver part which we will call RF part; 2) transmitting radio frequency control and amplifying reflected signal part which we will call electric part; and 3) data collection, data processing, and visualization part which we will call post-processing part. A transmitting radio frequency control and an amplifying reflected signal part are based on a research performed at a lecture and labs designed by researchers at Massachusetts Institute of Technology (MIT) Lincoln Lab, Charvat et al. (2012) and another lecture and labs designed by a professor at University of California at Davis, Liu (2013). The radar system designed at University of California at Davis is based on the system designed at MIT Lincoln Lab that proposed a design of a small, low cost, and low power consuming radar. The low power radar proposed by MIT Lincoln Lab is suitable to operate in any public places without any restrictions for human health because of it low power transmission, however surveillance area is relatively short and limited. To expand monitoring area with this proposed low power radar system, the transmit power of the radar system proposed in this study is enhanced comparing to the radar proposed by MIT Lincoln Lab. Additionally, the radar system is designed and fabricated on printed circuit boards (PCBs) to make the system compact and easy to access for use of various purposed of research fields. For instance, the radar system can be utilized for mapping, localization, or imaging. <div><br></div><div>The first part of post-processing is data collection. The raw data received and amplified through the electric part in the hardware is collected through a compact computer, a Raspberry Pi 3, that is directly connected to the radar. The data collected every second and the collected data is transferred to the post-processing devices, which is a laptop computer in this research. The post-processing device processes data to estimate range of the object, applies filters for tracking, and visualizes the results. In the study, a variant of the Advanced Message Queuing Protocol (AMQP) called RabbitMQ, also called as RMQ (Richardson, 2012; Videla & Williams, 2012) is utilized for real-time data transfer between the Raspberry Pi 3 and a post-processing device. Because each of the data collection, post-processing scripts, and visualization processing have to be performed continuously and sequentially, the RMQ has been used for data exchange between the processes to assist parallel data collection and processing. The processed data show an estimated distance of the object from the radar system in real-time, so that the system can support to monitor a certain area in a remote place if the two distant places are connected through a network.<div><br></div><div>This proposed radar system performed successfully to detect and track an object that was in the sight of the radar. Although further study to improve the system is required, the system will be highly suitable and applicable for research areas requiring sensors for exploration, monitoring, or surveillance because of its accessibility and flexibility. Users who will adopt this radar system for research purposes can develop their own applications that match their research environment for example to support robots for obstacle avoidance or localization and mapping.<br><div><div><div> </div> </div> </div></div></div></div></div>

This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario.

The electronics newspaper becomes a main source for online news readers. When facing the numerous stories, news readers need some supports in order to review a topic in short time. Due to previous researches in TDT (Topic Detection and Tracking) only considering how to identify events and present the results with news titles and keywords, a summarized text to present event evolution is necessary for general news readers to retrospect events under a news topic. This thesis proposes a topic retrospection process and implements the SToRe system that identifies various events under a new topic and constructs the relationship to compose a summary which gives readers the sketch of event evolution in a topic. It consists of three main functions: event identification, main storyline construction and storyline-based summarization. The constructed main storyline can remove the irrelevant events and present a main theme. The summarization extracts the representative sentences and takes the main theme as the template to compose summary. The summarization not only provides enough information to comprehend the development of a topic, but also can be an index to help readers to find more detailed information. A lab experiment is conducted to evaluate the SToRe system in the question-and-answer (Q&A) setting. From the experimental results, the SToRe system can help news readers more effectively and efficiently to capture the development of a topic.

Passive acoustic systems composed of hydrophone array have been shown useful for underwater acoustic source detection and tracking. The work presented here demonstrates use of a passive acoustic system for an Autonomous Underwater Vehicle (AUV) composed of a 2D hydrophone array along with a post processing algorithm for real time detection and tracking of surface vessels. Important design decisions for development of the hydrophone array are taken based on different factors such as the frequency range of broadband surface vessel noise, review of literature, financial as well as structural constraints of the AUV. The post-processing algorithm, developed using a phased array principle called acoustic beamforming, outputs real-time heading angles of the target surface vessels. Initial measurements conducted at Claytor Lake with the developed passive acoustic system to locate a white noise acoustic source showed better performance with functional beamforming technique among others. Various hydrophone array configurations are tested during these measurements to determine the optimal hydrophone placement. Furthermore, field tests are conducted at Norfolk Bay area to assess the performance of the developed system to real time detect and track surface vessels of different sizes in mission relevant environment. Cross-spectral matrix subtraction approach to subtract AUV's self noise is investigated to improve signal range and thus the detection range of these different surface vessels. This approach showed improvement in detection range of up to 350%. Another set of measurements again at Claytor Lake demonstrates real time detection and tracking of a small boat using an AUV integrated with the developed passive acoustic system operating at different propeller conditions. Results showed that low signal to noise ratio at higher AUV propeller rpm makes the detection and tracking difficult limiting the operating AUV propeller rpm up to 1500. This work also explores custom build hydrophones based on piezoelectric material of different shapes and sized to replace the expensive industry purchased hydrophones to lower the cost of developed system. / Master of Science / In field of underwater acoustic, hydrophone arrays have gained popularity for the detection and tracking of sound sources by just listening to them. This study presents design, development and testing of such hydrophone array attached to an AUV for real time detection and tracking of surface vessels. Multiple hydrophones in an array collect the underwater noise radiated by the target surface vessel which are essentially the unsteady pressure fluctuations. The phase difference between signals acquired by different hydrophones is then used to predict the direction of arrival of a sound wave from the target ship. Such a phased array principle called acoustic beamforming is used to develop a post processing algorithm which takes hydrophone array signals as input and outputs the heading angle of the target ship. This work first demonstrates capability of the developed hydrophone array and the algorithm to detect a white noise acoustic source (speaker) placed inside water at Claytor Lake. These measurements investigated performance of different acoustic beamforming techniques as well as different hydrophone array configurations. Furthermore, measurements conducted with actual surface vessel at Norfolk Bay area proved capability of the developed hydrophone array and the algorithm to detect and track ships in real time. The performance of the hydrophone array is characterized in terms of detection range and was observed to improve by 350% when the AUV's self noise is removed from the acquired hydrophone signals. Combined single unit of AUV and developed hydrophone array system also demonstrated real time detection and tracking of a small boat at Claytor Lake for different AUV operating conditions. Moreover, custom build hydrophones manufactured using piezoelectric material are found to be a feasible replacement for the expensive industry purchased hydrophones in order to reduce cost of the array.

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

No description available.

Stent implantation is the most common treatment of coronary heart disease, one of the major causes of death worldwide. During a stenting procedure, the clinician inserts interventional devices inside the patient's vasculature. The navigation of the devices inside the patient's anatomy is monitored in real-time, under X-ray fluoroscopy. Three specific interventional devices play a key role in this procedure: the guide-wire, the angioplasty balloon and the stent. The guide-wire appears in the images as a thin curvilinear structure. The angioplasty balloon, that has two characteristic markerballs at its extremities, is mounted on the guide-wire. The stent is a 3D metallic mesh, whose appearance is complex in the fluoroscopic images. Stents are barely visible, but the proper assessment of their deployment is key to the procedure. The objective of the work presented in this thesis is twofold. On the first hand, we aim at designing, studying and validating image processing techniques that improve the visualization of stents. On the second hand, we study the processing of curvilinear structures (like guide-wires) for which we propose a new image processing technique. We present algorithms dedicated to the 2D and 3D visualization of stents. Since the stent is hardly visible, we do not intend to directly locate it by image processing means in the images. The position and motion of the stent are inferred from the location of two landmarks: the angioplasty balloon and of the guide-wire, which have characteristic shapes. To this aim, we perform automated detection, tracking and registration of these landmarks. The cornerstone of our 2D stent visualization enhancement technique is the use of the landmarks to perform motion compensated noise reduction. We evaluated the performance of this technique for 2D stent visualization over a large database of clinical data (nearly 200 cases). The results demonstrate that our method outperforms previous state of the art techniques in terms of image quality. A comprehensive validation confirmed that we reached the level of performance required for the commercial introduction of our algorithm. It is currently deployed in a large number of clinical sites worldwide. The 3D stent visualization that we propose, uses the landmarks to achieve motion compensated tomographic reconstruction. We show preliminary results over 22 clinical cases. Our method seems to outperform previous state of the art techniques both in terms of automation and image quality. The previous stent visualization methods involve the segmentation of the part of the guide-wire extending through the stent. We propose a generic tool to process such curvilinear structures that we call the Polygonal Path Image (PPI). The PPI relies on the concept of locally optimal paths. One of its main advantages is that it unifies the concepts of several previous state of the art techniques in a single formalism. Moreover the PPI enables to control the smoothness and the length of the structures to segment. Its parametrization is simple and intuitive. In order to fully benefit from the PPI, we propose an efficient scheme to compute it. We demonstrate its applicability for the task of automated guide-wire segmentation, for which it outperforms previous state of the art techniques

L'objectif de cette thèse est de proposer une approche hiérarchique pour la reconnaissance de la prise de médicaments chez les personnes âgées. En effet, l'activité globale de la prise de médicaments se compose de plusieurs activités à différents niveaux de complexité.La reconnaissance est donc faite de bas en haut, de l'activité élémentaire à l'activité simple et ensuite à l'activité complexe. De plus, un modèle simple de calibration, utilisant une caméra stéréo, est proposé pour estimer la profondeur des objets, et ainsi mieux traiter l'occultation des objets. Par conséquence, la reconnaissance de la prise de médicaments est plus précise.Premièrement, la méthode de soustraction du fond est utilisée pour détecter les objets mobiles, dans un environnement intérieur. La segmentation des régions de peau, et des flacons se fait ensuite en utilisant l'information de couleur par seuillage.Deuxièmement, en observant que le déplacement des régions de peau dans deux trames consécutives est petit, nous utilisons la distance minimale de déplacement pour suivre les régions de peau. Les régions des mains sont détectées en exploitant l'intensité de contours.Nous détectons la bouche par la méthode AdaBoost et le suivi de bouche se fait en utilisant le filtre de Kalman et le ratio des couleurs R/G. Le filtre de Kalman est aussi utilisé pour le traitement d'occultation entre les régions d'intérêt main-visage, main-main.Finalement, pour la reconnaissance de la prise de médicaments, une approche hiérarchique est proposée, en commençant par les activités élémentaires. Sur la base du chevauchement entre les régions d'intérêt, nous détectons les activités élémentaires. En exploitant la séquence des activités élémentaires, nous détectons les activités simples, celles-ci sont en suite utilisées pour reconnaître des activités complexes, correspondant à la prise de médicaments. La profondeur des objets occultés est estimée afin de vérifier l'état de contact entre ces objets, et reconnaître plus précisément les activités.L'expérience montre que notre approche est plus robuste et souple que les travaux précédents sur le sujet. Elle permet de reconnaître des scénarios différents de prise de médicaments et peut être appliqué pour reconnaître d'autres activités complexes en général. / The objective of this thesis is to propose a hierarchical approach for recognition of themedication intake for elderly people. By analyzing the complex activity of the medicationintake we show that it consists of several activities, from low of high levels. So recognition ismade from top to bottom, from primary activity to simple activity and then complex activity.In addition, a simple calibration model, using a stereo camera is proposed to estimatethe depth of objects, for better handling of object occlusions. Consequently, the recognitionof the medication intake is more accurate.First of all, a background subtraction method is used to detect moving objects in theindoor environment. The segmentation of skin regions, and medication bottles is made usingcolor information, by thresholding.Secondly, by observing that the displacement of skin regions in two consecutive frames issmall, we use the minimum distance of displacement to track the skin regions. The regionsof hands are detected by exploiting the intensity contours. We detect the mouth by theAdaBoost method and the tracking of mouth is done using the Kalman filter and the ratioof colors R/G. The Kalman filter is also used for handling occlusions of regions of interest,between hand-face, and hand-hand.Finally, for the recognition of the medication intake, a hierarchical approach is proposed,based on primary activities. By detecting the overlap between the regions of interest, weidentify the primary activities. By exploiting the sequence of primary activities, we recognizesimple activities, that are inputs for recognizing complex activities, which correspond tomedication intake. The depth of occluded objects is estimated at the end to check thecontact state between these objects, to recognize more precisely the activities.Experience showed that our approach is more robust and flexible than prior works inthe literature on this subject. It allows to recognize different scenarios of medication intakeand can be applied to recognize other complex activities in general.

Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of larger robotic systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. The proposed self-reconfigurable mobile robot design exhibits dual mobility using a tracked drive for longitudinal locomotion and wheeled drive for lateral locomotion. The two degrees of freedom (DOF) docking interface referred to as GHEFT (Genderless, High strength, Efficient, Fail-Safe, high misalignment Tolerant) allows for an efficient docking while tolerating misalignments in 6-DOF. In addition, motion along the vertical axis is also achieved via an additional translational DOF, allowing for toggling between tracked and wheeled locomotion modes by lowering and raising the wheeled assembly. This thesis also presents a visual-based onboard Hybrid Target Tracking algorithm to detect and follow a target robot leading to autonomous docking between the modules. As a result of this proposed approach, the tracked features are then used to bring the robots in sufficient proximity for the docking procedure using Image Based Visual Servoing (IBVS) control. Experimental results to validate the robustness of the proposed tracking method, as well as the reliability of the autonomous docking procedure, are also presented in this thesis. / Master of Science / Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of larger robotic systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. Such robots can prove useful in environments that are either too dangerous or inaccessible to humans. Therefore, in this research, several specific hardware and software development aspects related to self-reconfigurable mobile robots are proposed. In terms of hardware development, a robotic module was designed that is symmetrically invertible and exhibits dual mobility using a tracked drive for longitudinal locomotion and wheeled drive for lateral locomotion. Such interchangeable mobility is important when the robot operates in a constrained workspace. The mobile robot also has integrated two degrees of freedom (DOF) docking mechanisms referred to as GHEFT (Genderless, High strength, Efficient, Fail-Safe, high misalignment Tolerant). The docking interface allows for an efficient docking while tolerating misalignments in 6-DOF. In addition, motion along the vertical axis is also performed via an additional translational DOF, allowing for lowering and raising the wheeled assembly. The robot is equipped with sensors to provide positional feedback of the joints relative to the target robot. In terms of software development, a visual-based onboard Hybrid Target Tracking algorithm for high-speed consistent tracking iv of colored targets is also presented in this work. The proposed technique is used to detect and follow a colored target attached to the target robot leading to autonomous docking between the modules using Image Based Visual Servoing (IBVS). Experimental results to validate the robustness of the proposed tracking approach, as well as the reliability of the autonomous docking procedure, are also presented in the thesis. The thesis is concluded with discussions about future research in both structured and unstructured terrains.