Improving 3d pedestrian detection for wearable sensor data with 2d human pose

Collisions and safety are important concepts when dealing with urban designs like shared spaces. As pedestrians (especially the elderly and disabled people) are more vulnerable to accidents, realising an intelligent mobility aid to avoid collisions is a direction of research that could improve safety using a wearable device. Also, with the improvements in technologies for visualisation and their capabilities to render 3D virtual content, AR devices could be used to realise virtual infrastructure and virtual traffic systems. Such devices (e.g., Hololens) scan the environment using stereo and ToF (Time-of-Flight) sensors, which in principle can be used to detect surrounding objects, including dynamic agents such as pedestrians. This can be used as basis to predict collisions. To envision an AR device as a safety aid and demonstrate its 3D object detection capability (in particular: pedestrian detection), we propose an improvement to the 3D object detection framework Frustum Pointnet with human pose and apply it on the data from an AR device. Using the data from such a device in an indoor setting, we conducted a comparative study to investigate how high level 2D human pose features in our approach could help to improve the detection performance of orientated 3D pedestrian instances over Frustum Pointnet

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model

INDOOR 3D MODELING AND FLEXIBLE SPACE SUBDIVISION FROM POINT CLOUDS

Indoor navigation can be a tedious process in a complex and unknown environment. It gets more critical when the first responders try to intervene in a big building after a disaster has occurred. For such cases, an accurate map of the building is among the best supports possible. Unfortunately, such a map is not always available, or generally outdated and imprecise, leading to error prone decisions. Thanks to advances in the laser scanning, accurate 3D maps can be built in relatively small amount of time using all sort of laser scanners (stationary, mobile, drone), although the information they provide is generally an unstructured point cloud. While most of the existing approaches try to extensively process the point cloud in order to produce an accurate architectural model of the scanned building, similar to a Building Information Model (BIM), we have adopted a space-focused approach. This paper presents our framework that starts from point-clouds of complex indoor environments, performs advanced processes to identify the 3D structures critical to navigation and path planning, and provides fine-grained navigation networks that account for obstacles and spatial accessibility of the navigating agents. The method involves generating a volumetric-wall vector model from the point cloud, identifying the obstacles and extracting the navigable 3D spaces. Our work contributes a new approach for space subdivision without the need of using laser scanner positions or viewpoints. Unlike 2D cell decomposition or a binary space partitioning, this work introduces a space enclosure method to deal with 3D space extraction and non-Manhattan World architecture. The results show more than 90% of spaces are correctly extracted. The approach is tested on several real buildings and relies on the latest advances in indoor navigation

Constraints, Histones, and the 30 Nanometer Spiral

We investigate the mechanical stability of a segment of DNA wrapped around a histone in the nucleosome configuration. The assumption underlying this investigation is that the proper model for this packaging arrangement is that of an elastic rod that is free to twist and that writhes subject to mechanical constraints. We find that the number of constraints required to stabilize the nuclesome configuration is determined by the length of the segment, the number of times the DNA wraps around the histone spool, and the specific constraints utilized. While it can be shown that four constraints suffice, in principle, to insure stability of the nucleosome, a proper choice must be made to guarantee the effectiveness of this minimal number. The optimal choice of constraints appears to bear a relation to the existence of a spiral ridge on the surface of the histone octamer. The particular configuration that we investigate is related to the 30 nanometer spiral, a higher-order organization of DNA in chromatin.Comment: ReVTeX, 15 pages, 18 figure

Prognosis in patients with myocardial infarction with ST-elevation depending on the timing of interventional revascularization

Проверена е прогнозата (болничния и следболничния леталитет до края на 6-ия месец) при 300 болни (212 мъже и 88 жени) с първи миокарден инфаркт със ST- елевация (STEMI) на средна възраст 62.9 год. в зависимост от срока на извършената първична коронарна интервенция (PCI) след началото на симптомите. В зависимост от срока на извършената РСІ болните са разделени на 4 групи: до 3-ия, до 6-ия, до 12-ия и до 24-ия час след началото на инфаркта. Болничният леталитет за всички болни е 6.3%, a до края на 6-ия месец - 13.3%, еднакъв при І-ва и ІІ-ра група и достоверно по-малък, отколкото при ІІІ-та и ІV-та група, по-голям при жените, при болните над 65 г., с ФИ <35.0% и с тромботична оклузия на LM и LAD.The prognosis (in-hospital and post-hospitalization lethality by the end of the 6th moth) of 300 patients (212 men and 88 women) with a first myocardial infarction with ST-elevation (STEMI) at an average age of 62.9 years was studied depending on the timing of the conducted primary coronary intervention (PCI) after the onset of symptoms. Depending on the timing of the conducted PCI, the patients were divided into 4 groups: by the 3rd, 6th, 12th, and 24th hour after the onset of the infarction. The patients` in-hospital lethality was 6.3%, and that by the end of the 6th month - 13.3%. It was the same for groups I and II and significantly lower than in groups III and IV; higher in women, in patients over 65 years of age, with ejection fraction (EF) <35.0% and with thrombotic occlusion of LM and LAD

Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement

CAPACITY BUILDING FOR GIS-BASED SDG INDICATORS ANALYSIS WITH GLOBAL HIGH-RESOLUTION LAND COVER DATASETS

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the ”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement

Integrating Geodesign and game experiments for negotiating urban development

In this article we explore an expansion of geodesign to analyze processes of competition and cooperation by combining it with game-theoretical modelling and experiments. We test the applicability of facilitating these two fields in an integrated workshop by analysing the case study of oversupply of development sites in the Liemers corridor. Two workshops were held, with representatives of the six municipalities involved and with the regional and provincial authority, in which participants negotiated over the distribution of the supply of development sites. The workshops were performed around an interactive MapTable, with spatial information (from GIS) and financial information (from the game-theoretical model) being visualized in real-time. The integrated workshops were assessed to discover differences in terms of process and outcomes, and they examine whether and how learning takes place. We conclude that the combination of game theory and geodesign provides added value for planning support by facilitating a realistic discussion, and negotiation that is strongly connected to real-life locations, and by aiming at designing a common, collaborative solution. Through the integrated workshop learning about the problem of oversupply in financial and geographical terms and also about each other’s motives and behaviour is stimulated

3D City Models and urban information: Current issues and perspectives

Considering sustainable development of cities implies investigating cities in a holistic way taking into account many interrelations between various urban or environmental issues. 3D city models are increasingly used in different cities and countries for an intended wide range of applications beyond mere visualization. Could these 3D City models be used to integrate urban and environmental knowledge? How could they be improved to fulfill such role? We believe that enriching the semantics of current 3D city models, would extend their functionality and usability; therefore, they could serve as integration platforms of the knowledge related to urban and environmental issues allowing a huge and significant improvement of city sustainable management and development. But which elements need to be added to 3D city models? What are the most efficient ways to realize such improvement / enrichment? How to evaluate the usability of these improved 3D city models? These were the questions tackled by the COST Action TU0801 “Semantic enrichment of 3D city models for sustainable urban development”. This book gathers various materials developed all along the four year of the Action and the significant breakthroughs