High-speed object detection with a single-photon time-of-flight image sensor

3D time-of-flight (ToF) imaging is used in a variety of applications such as augmented reality (AR), computer interfaces, robotics and autonomous systems. Single-photon avalanche diodes (SPADs) are one of the enabling technologies providing accurate depth data even over long ranges. By developing SPADs in array format with integrated processing combined with pulsed, flood-type illumination, high-speed 3D capture is possible. However, array sizes tend to be relatively small, limiting the lateral resolution of the resulting depth maps, and, consequently, the information that can be extracted from the image for applications such as object detection. In this paper, we demonstrate that these limitations can be overcome through the use of convolutional neural networks (CNNs) for high-performance object detection. We present outdoor results from a portable SPAD camera system that outputs 16-bin photon timing histograms with 64x32 spatial resolution. The results, obtained with exposure times down to 2 ms (equivalent to 500 FPS) and in signal-to-background (SBR) ratios as low as 0.05, point to the advantages of providing the CNN with full histogram data rather than point clouds alone. Alternatively, a combination of point cloud and active intensity data may be used as input, for a similar level of performance. In either case, the GPU-accelerated processing time is less than 1 ms per frame, leading to an overall latency (image acquisition plus processing) in the millisecond range, making the results relevant for safety-critical computer vision applications which would benefit from faster than human reaction times.Comment: 13 pages, 5 figures, 3 table

Ingeniería Geológica en Terrenos Volcánicos. Métodos, Técnicas y Experiencias en las Islas Canarias

La presente obra es un compendio de conceptos, metodologías y técnicas útiles para acometer proyectos y obras en terrenos volcánicos desde el punto de vista de la ingeniería geológica y la geotecnia. El libro se presenta en tres partes diferenciadas. La primera es conceptual y metodológica, con capítulos que tratan sobre la clasificación de las rocas volcánicas con fines geotécnicos, la caracterización geomecánica, los problemas geotécnicos y constructivos asociados a los distintos materiales, y una guía metodológica para la redacción de informes geotécnicos para la edificación. La segunda parte aborda las aplicaciones a obras de ingeniería, incluyendo deslizamientos, obras subterráneas,infraestructuras marítimas y obras públicas. La tercera parte recoge capítulos dedicados a describir distintos casos prácticos de obras y proyectos en los que la problemática geotécnica en terrenos volcánicos ha tenido un papel relevante. Los capítulos han sido elaborados por técnicos y científicos de reconocido prestigio en el campo de la ingeniería geológica en terrenos volcánicos, que han plasmado en ellos sus conocimientos y experiencias en la materia.Los editores y autores de parte de los capítulos del libro, los Doctores Luis E. Hernández Gutiérrez (Geólogo) y Juan Carlos Santamarta Cerezal (Ingeniero de Montes, Civil y Minas), son los responsables del grupo de investigación INGENIA (Ingeniería Geológica, Innovación y Aguas). Su actividad investigadora comprende más de 200 publicaciones en el área de la ingeniería geológica, la geotecnia, medio ambiente y el aprovechamiento del agua en islas y terrenos volcánicos. En relación a la docencia han impartido y dirigido más de 90 seminarios y cursos de especialización a nivel nacional e internacional, incluyendo la organización de 4 congresos internacionales. Fueron premiados por la Universidad de La Laguna en los años 2012, 2013 y 2014 por su calidad docente e innovación universitaria, y son pioneros en los laboratorios virtuales para la enseñanza de la ingeniería. Participan activamente como profesores colaboradores e investigadores en varias universidades e instituciones españolas e internacionales. Todas sus publicaciones están disponibles en internet, con libre acceso. Ingeniería geológica en terrenos volcánicos, es una obra de gran interés para, consultores, técnicos de administraciones públicas, proyectistas y demás profesionales implicados en obras y proyectos de infraestructuras en terrenos volcánicos; también es útil para académicos y estudiantes de ingeniería o ciencias geológicas que quieran investigar o iniciarse en las singularidades que presentan los materiales volcánicos en la edificación o en la ingeniería civil y minera

Requisitos de un Sistema de Información para Gestión de Patrimonio

[ES] El área del Patrimonio Cultural ha tenido un gran avance en los últimos años. Actualmente se están desarrollando nuevas métodos y herramientas como consecuencia del traspaso de conocimiento que se está produciendo. Una de estas posibilidades es la de crear un sistema para Patrimonio Cultural con las ventajas de un Sistema de Información Geográfica. Las dificultades no son nimias, consistiendo el primer paso en crear un marco de requerimientos comunes para las múltiples necesidades que se pueden presentar. En este trabajo presentamos un conjunto de requerimientos que cumple dicho objetivo, obtenido a partir del estudio de las necesidades de varios equipos de restauradores y profesionales del Patrimonio Histórico.[EN] Cultural Heritage has had a great development in recent years. Currently, new tools are being developed as a result of transferring knowledge between areas. One of these possibilities is to create a system for Cultural Heritage with the advantages of Geographic Information System. The difficulties are not trivial, being the first step to create a framework of common requirements for the multiple needs that may arise. We present a set of requirements that provide that objective, obtained from the study of the needs of various restoration and Cultural Heritage professional teams.Este trabajo ha sido parcialmente financiado por la Consejería de Innovación Ciencia y Empresa de la Junta de Andalucía a través del proyecto de excelencia PE09-TIC-5276Luzón, MV.; Martín Perandrés, D.; Arroyo, G.; López Rodríguez, JR.; Herce Fimia, J.; Izquierdo De Montes, R.; Jiménez Sancho, Á.... (2012). Requisitos de un Sistema de Información para Gestión de Patrimonio. Virtual Archaeology Review. 3(5):43-47. https://doi.org/10.4995/var.2012.4498OJS434735AGNELLO, F. et al. (2003): "Cultural heritage and information systems, an investigation into a dedicated hypertext". The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 34, 5.CIGNONI, P. et al. (2008): "Meshlab: an open-source mesh processing tool". En Sixth Eurographics Italian Chapter Conference, pp. 129-136.CIGNONI, P. et al. (2008): "Meshlab: an open-source 3D mesh processing system". ERCIM News, 73, pp. 45-46.HODAČ, J. (2005): "3D information system of historical site. Proposal and Realisation of a Functional Prototype". Acta Polytechnica 45, 1.IOANNIDIS, C. et al. (2003): "An integrated spatial information system for the development of the archaeological site of mycenae". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34, 5.LAMOLDA, F. et al. (2008): "Registro mediante la utilizacion de escaner 3D del estado previo a la intervencion de la fuente de los leones". En Taller en el IX Congreso Internacional de Rahabilitacion del Patrimonio Arquitectonico y Edificacion - Sevilla, Espana.MEYER, E. et al. (2006): "Intrasite level cultural heritage documentation: Combination of survey, modeling and imagery data in a web information system". En 7th International Symposium on Virtual Reality, Archaeology and Cultural Heritage.MEYER, E. et al. (2007): "A web information system for the management and the dissemination of cultural heritage data". Journal of Cultural Heritage 8, pp. 396-411. http://dx.doi.org/10.1016/j.culher.2007.07.003NAGLIČ, K. (2003): "Cultural heritage information system in the republic of Slovenia". En ARIADNE 5 Workshop on Documentation, Interpretation, Presentation and Publication of Cultural Heritage. Prague.TORRES, J.C. et al. (2007): "Generacion automatizada de modelado 3D para difusion y documentacion del patrimonio historico". En I Simposium de Informatica Grafica e Patrimonio Historico, La Coruna, pp. 111-120.VLC: VISUAL COMPUTING LAB. http://vcg.isti.cnr.it

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

Deep learning-enhanced 3D direct time-of-flight imaging

Neural networks have revolutionised the field of computer vision thanks to their ability to learn and extract complex representations from high volumes of data. They have surpassed human-level performance in multiple fields, such as object detection or medical imaging analysis, and have opened up new applications in robotics or autonomous driving. Similarly, the use of single-photon avalanche diode (SPAD) 3D depth sensors based on time-of-flight (ToF) has become widespread in recent years. These sensors are found in smartphones, AR/VR technology, and even home appliances. SPADs have also become a key technology in LIDAR for autonomous systems. By integrating SPAD arrays with processing logic, solid-state, all-digital receivers can be implemented that provide accurate depth maps even in high ambient conditions. However, array sizes in direct time-of-flight SPADs tend to be limited, resulting in relatively low angular resolution when imaging in a flash modality. There is therefore an interest in using post-processing with neural networks to improve the lateral resolution of depth maps, as well as to provide scene interpretation, especially for targets at longer distances subject to significant pixelation. An algorithm trading frame rate for increased lateral resolution (×4) from a SPAD direct ToF (dToF) sensor (64×32 pixels) is explored. This algorithm combines multiple frames subject to camera motion and proves to be effective especially in scenes with low signal-to-background ratio conditions. However, its performance can deteriorate for global scene motion. Thus, a more robust and general approach is developed using 3D convolutional neural networks (CNN) trained on purely synthetic data to denoise and upscale (×4) depth information. Experimental results demonstrate an improvement in the quality of depth maps, with frames processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance. Limitations in object detection caused by the low lateral resolution of depth sensors can be overcome using CNNs. Results obtained at short range with exposure times as low as 2 ms (equivalent to 500 FPS), signal-to-background ratios as low as 0.05, and processing times under 1 ms per frame provide evidence of high-accuracy detection, particularly when using full temporal histogram data rather than depth or intensity information alone. High-level vision tasks studied by performing human activity recognition based on depth information using long short-term memory (LSTM) neural networks. This network demonstrates high accuracy (89%) and processing latency of 150 ms, even when people are represented by only a few pixels at distances exceeding 30 metres

Human activity recognition using a single-photon direct time-of-flight sensor

Single-Photon Avalanche Diode (SPAD) direct Time-of-Flight (dToF) sensors provide depth imaging over long distances, enabling the detection of objects even in the absence of contrast in colour or texture. However, distant objects are represented by just a few pixels and are subject to noise from solar interference, limiting the applicability of existing computer vision techniques for high-level scene interpretation. We present a new SPAD-based vision system for human activity recognition, based on convolutional and recurrent neural networks, which is trained entirely on synthetic data. In tests using real data from a 64×32 pixel SPAD, captured over a distance of 40 m, the scheme successfully overcomes the limited transverse resolution (in which human limbs are approximately one pixel across), achieving an average accuracy of 89% in distinguishing between seven different activities. The approach analyses continuous streams of video-rate depth data at a maximal rate of 66 FPS when executed on a GPU, making it well-suited for real-time applications such as surveillance or situational awareness in autonomous systems