The Sheffield Search and Rescue corpus

© 2017 IEEE. As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation is about a collaborative task of exploring and estimating a simulated indoor environment. The task has carefully been designed to have a quantitative measure for the amount of exchanged information about the discourse subject. SSAR includes different layers of annotations which should be of interest to researchers in a wide range of human/human conversation understanding as well as automatic speech recognition. It also provides an amount of data for analysis of multiple parallel conversations around a single subject. The SSAR corpus is available for research purposes

Deriving and Exploiting Situational Information in Speech: Investigations in a Simulated Search and Rescue Scenario

The need for automatic recognition and understanding of speech is emerging in tasks involving the processing of large volumes of natural conversations. In application domains such as Search and Rescue, exploiting automated systems for extracting mission-critical information from speech communications has the potential to make a real difference. Spoken language understanding has commonly been approached by identifying units of meaning (such as sentences, named entities, and dialogue acts) for providing a basis for further discourse analysis. However, this fine-grained identification of fundamental units of meaning is sensitive to high error rates in the automatic transcription of noisy speech. This thesis demonstrates that topic segmentation and identification techniques can be employed for information extraction from spoken conversations by being robust to such errors. Two novel topic-based approaches are presented for extracting situational information within the search and rescue context. The first approach shows that identifying the changes in the context and content of first responders' report over time can provide an estimation of their location. The second approach presents a speech-based topological map estimation technique that is inspired, in part, by automatic mapping algorithms commonly used in robotics. The proposed approaches are evaluated on a goal-oriented conversational speech corpus, which has been designed and collected based on an abstract communication model between a first responder and a task leader during a search process. Results have confirmed that a highly imperfect transcription of noisy speech has limited impact on the information extraction performance compared with that obtained on the transcription of clean speech data. This thesis also shows that speech recognition accuracy can benefit from rescoring its initial transcription hypotheses based on the derived high-level location information. A new two-pass speech decoding architecture is presented. In this architecture, the location estimation from a first decoding pass is used to dynamically adapt a general language model which is used for rescoring the initial recognition hypotheses. This decoding strategy has resulted in a statistically significant gain in the recognition accuracy of the spoken conversations in high background noise. It is concluded that the techniques developed in this thesis can be extended to more application domains that deal with large volumes of natural spoken conversations

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

First responders occupancy, activity and vital signs monitoring - SAFESENS

This paper describes the development and implementation of the SAFESENS (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) location tracking and first responder monitoring demonstrator. An international research collaboration has developed a stateof-the-art wireless indoor location tracking system for first responders, focused initially on fire fighter monitoring. Integrating multiple gas sensors and presence detection technologies with building safety sensors and personal monitors has resulted in more accurate and reliable fire and occupancy detection information. This is invaluable to firefighters in carrying out their duties in hostile environments. This demonstration system is capable of tracking occupancy levels in an indoor environment as well as the specific location of fire fighters within those buildings, using a multi-sensor hybrid tracking system. This ultra-wideband indoor tracking system is one of the first of itsâ kind to provide indoor localization capability to sub meter accuracies with combined Bluetooth low energy capability for low power communications and additional inertial, temperature and pressure sensors. This facilitates increased precision in accuracy detection through data fusion, as well as the capability to communicate directly with smartphones and the cloud, without the need for additional gateway support. Glove based, wearable technology has been developed to monitor the vital signs of the first responder and provide this data in real time. The helmet mounted, wearable technology will also incorporate novel electrochemical sensors which have been developed to be able to monitor the presence of dangerous gases in the vicinity of the firefighter and again to provide this information in real time to the fire fighter controller. A SAFESENS demonstrator is currently deployed in Tyndall and is providing real time occupancy levels of the different areas in the building, as well as the capability to track the location of the first responders, their health and the presence of explosive gases in their vicinity. This paper describes the system building blocks and results obtained from the first responder tracking system demonstrator depicted

Information systems architecture for fire emergency response

Purpose There has been a lack of meaningful information systems architecture, which comprehensively conceptualise the essential components and functionality of an information system for fire emergency response addressing needs of different job roles. This study proposes a comprehensive information systems architecture which would best support four of the key fire-fighter job roles. Design The study has built on the outcomes of two previous preliminary studies on information and human-computer interaction needs of core fire fighter job roles. Scenario based action research was conducted with fire fighters in a range of roles, to evaluate human computer interaction needs while using various technology platforms. Findings Several key themes were identified and led us to propose several layers of an integrated architecture, their composition and interactions. Research limitations The selected fire scenarios may not represent every type of fire expected in high risk built environments. Practical implications The current paper represents a shared discussion among end users, system architects and designers, to understand and improve essential components. It, therefore, provides a reference point for the development of an information system architecture for fire emergency response. Originality The proposed information system architecture is novel because it outlines specific architectural elements required to meet the specific situation awareness needs of four of the key firefighters job roles

Occlusion-based computational periscopy with consumer cameras

The ability to form images of scenes hidden from direct view would be advantageous in many applications – from improved motion planning and collision avoidance in autonomous navigation to enhanced danger anticipation for first-responders in search-and-rescue missions. Recent techniques for imaging around corners have mostly relied on time-of-flight measurements of light propagation, necessitating the use of expensive, specialized optical systems. In this work, we demonstrate how to form images of hidden scenes from intensity-only measurements of the light reaching a visible surface from the hidden scene. Our approach exploits the penumbra cast by an opaque occluding object onto a visible surface. Specifically, we present a physical model that relates the measured photograph to the radiosity of the hidden scene and the visibility function due to the opaque occluder. For a given scene–occluder setup, we characterize the parts of the hidden region for which the physical model is well-conditioned for inversion – i.e., the computational field of view (CFOV) of the imaging system. This concept of CFOV is further verified through the Cram´er–Rao bound of the hidden-scene estimation problem. Finally, we present a two-step computational method for recovering the occluder and the scene behind it. We demonstrate the effectiveness of the proposed method using both synthetic and experimentally measured data.Accepted manuscrip

Risk analysis for flood event management

PhD ThesisFlood risk management seeks to reduce flood consequences and probability by considering a wide range of options that include non-structural measures such as flood event management. Quantitative flood risk analysis has provided a powerful tool to support appraisal and investment in engineered flood defence. However, analysing the risks and benefits of non-structural measures have been limited making it difficult to compare the benefits of a wide range of options on a shared assessment platform. A major challenge to understand the performance of non-structural measures during a flood event is the complexity of analysing the human responses in the system that determines the successful operation of flood event management. Here presents a risk analysis approach that couples a multi-agent simulation of individual and organizational behaviour with a hydrodynamic model. The model integrates remotely sensed information on topography, buildings and road networks with empirical survey data and information on local flood event management strategies to fit characteristics of specific communities. The model has been tested in Towyn, North Wales, and subsequently used to analyse the effectiveness of flood event management procedures, including flood warning and evacuation procedures in terms of potential loss of life , economic damages and the identification of roads susceptible to congestion. The potential loss of life increases according to the magnitude of a storm surge (e.g. 11 for 1 in 100 years surges as opposed to 94 for 1 in 1000 surges). Providing 3 hours flood warning can reduce this by 67% if individuals take appropriate action. A global sensitivity analysis shows that hydrodynamic processes are only responsible for 50% of the variance in expected loss of life because actions taken by individuals and society can greatly influence the outcome. The model can be used for emergency planners to improve flood response in a flood event.EPSRC studentshi

Multi-Relationship Evaluation Design (MRED): An Interactive Test Plan Designer for Advanced and Emerging Technologies

Ground-breaking technologies are developed for use across a broad range of domains such as manufacturing, military, homeland security and automotive industries. These advanced technologies often include intelligent systems or robotic elements. Evaluations are a critical step in the development of these advanced systems. Evaluation events inform the technology developers of specific needs for enhancement, capture end-user feedback, and verify the extent of the technology's functions. Test exercises are an opportunity to showcase the technology's current abilities and limitations and provide data for future test efforts. The objective of this research is to develop the Multi-Relationship Evaluation Design (MRED) methodology, an interactive test plan blueprint generator. MRED collects multiple inputs, processes them interactively with a test designer and outputs evaluation blueprints, specifying key test-plan characteristics. Drawing from the Systems Engineering Paradigm, MRED models a process that had not been modeled before. The MRED model is consistent with the experience of evaluation designers. This method also captures and handles stakeholder preferences so that they can be accommodated in a meaningful way. The result is the MRED methodology that combines practical evaluation design experience with mathematical methods proven in the literature

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Constructing informative Bayesian priors to improve SLAM map quality

The problem of Simultaneous Localisation And Mapping (SLAM) has been widely researched and has been of particular interest in recent years, with robots and self driving cars becoming ubiquitous. SLAM solutions to date have aimed to produce faster, more robust solutions that yield consistent maps by improving the filtering algorithms used, introducing better sensors, more efficient map representations or improved motion estimates. Whilst performing well in simplified scenarios, many of these solutions perform poorly in challenging real life scenarios. It is therefore important to produce SLAM solutions that can perform well even when using limited computational resources and performing a quick exploration for time critical operations such as Urban Search And Rescue missions. In order to address this problem this thesis proposes the construction of informative Bayesian priors to improve performance without adding to the computational complexity of the SLAM algorithm. Indoors occupancy grid SLAM is used as a case study to demonstrate this concept and architectural drawings are used as a source of prior information. The use of prior information to improve the performance of robotics systems has been successful in applications such as visual odometry, self-driving car navigation and object recognition. However, none of these solutions leverage prior information to construct Bayesian priors that can be used in recursive map estimation. This thesis addresses this problem and proposes a novel method to process architectural drawings and floor plans to extract structural information. A study is then conducted to identify optimal prior values of occupancy to assign to extracted walls and empty space. A novel approach is proposed to assess the quality of maps produced using different priors and a multi-objective optimisation is used to identify Pareto optimal values. The proposed informative priors are found to perform better than the commonly used non-informative prior, yielding an increase of over 20% in the F2 metric, without adding to the computational complexity of the SLAM algorithm