Real-Time Pedestrian Recognition in Urban Environments

2011 ◽  
pp. 139-147 ◽  
Author(s):  
Basam Musleh ◽  
Arturo de la Escalera ◽  
José Maria Armingol
Keyword(s):  
Real Time ◽  
Urban Environments

scholarly journals AquaVar: Real Time Models for Underground and Surface Waters Management at Catchment Scale

10.29007/mv2t ◽  
2018 ◽  
Author(s):  
Philippe Gourbesville ◽  
Marc Gaetano ◽  
Qiang Ma
Keyword(s):  
Real Time ◽  
Water Distribution ◽  
Current Situation ◽  
Urban Environments ◽  
Catchment Scale ◽  
Modeling Tools ◽  
Modeling Systems ◽  
Common Framework ◽  
Water Uses ◽  
Time Assessment

Management of water uses requests to harmonize demands and needs which are getting more complex and sophisticated. During the past 3 decades, modeling systems for hydrology, hydraulics and water quality have been used as stand alone products and were used in order to produce an analysis of a current situation and to generate forecast according to different horizons. The current situation requests an integration of the modeling tools into the information systems that are now dedicated to the global management of urban environments. Energy distribution, water distribution, solid wastes collection, traffic optimization are today major issues for cities that are looking for functional Decisions Supports Systems (DSSs) that may operate in a sustainable perspective. The basic requirement of real time assessment of the situation, the modeling systems identified as main elements of analytics and used for forecasts have to integrate a common framework allowing modular approach and interoperability. The paper presents the interest for a generic operational approach that could be implemented in order to address the management of water uses in a complex urban environment and to provide real time assessment and forecasts. The proposed approach is illustrated with application on Var catchment (3,000 km2) located in the French Riviera.

scholarly journals EXPERIENTIAL VIRTUAL URBAN ENVIRONMENTS: A PRELIMINARY INVESTIGATION

2018 ◽  
Vol 16 (5) ◽  
Author(s):  
Izham Ghani ◽  
Norhafizah Abdul Rahman ◽  
Nadiyanti Mat Nayan ◽  
Azrul Bahaluddin
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Preliminary Investigation ◽  
Urban Environments ◽  
Place Experience ◽  
Level Of Details ◽  
Preliminary Study ◽  
Virtual City ◽  
Being There ◽  
Contextual Setting

Virtual reality (VR) technologies enable users to be virtually immersed in reconstructed cities and streets from around the globe. Immersive technologies could provide users a suggestive sensation of “being there” in a reconstructed virtual urban environments (VUE). This research argues that experiential VUE could promote better understanding of a place while offering unique interactions within its surrounding elements. The aim of this research is to present a preliminary study of the factors determining place experience in a VUE. This research examines two related VUE case studies that offer real-time navigation via a 3D virtual environment (VE) platform to analyse the functionality of the offered interactions and user experience via its contents. Although preliminary investigations have shown some promising results in real-time virtual city walkthroughs, there are still some issues that still need to be addressed in order to provide experiential contents. Based on the findings, this research suggests future VUE improvements focusing on contextual setting, interactivity, navigation, level of details, viewpoints and auditory elements to provide an experiential walkthrough within a VUE. Findings from the case studies would assist and identify specific elements suitable for future development of more meaningful and experiential VUE in the Malaysian context.

scholarly journals Dynamic Resource Management of Real-Time Edge Services for Intelligent Vehicular Networks: A Case Study

2019 ◽  
Vol 25 (4) ◽  
pp. 58-61
Author(s):  
Katja Gilly ◽  
Sonja Filiposka ◽  
Salvador Alcaraz Carrasco ◽  
Anastas Mishev
Keyword(s):  
Resource Management ◽  
Real Time ◽  
Vehicular Networks ◽  
Urban Environments ◽  
City Centre ◽  
Time Service ◽  
Dynamic Resource Management ◽  
Dynamic Resource ◽  
Multi Access

The Internet of Vehicles requires high bandwidth and low latency services to unleash the potential of fully connected vehicles. Thus, the offloading proposals that successfully manage massive real-time service requests from vehicle nodes are needed. In this paper, we analyse the dynamic resource management for intelligent vehicular networks based on Multi-access Edge Computing architecture services. Using a combination of CloudSim and SUMO simulators, we present a case study of infotainment services in the city centre of Alicante, in Spain, that shows a high degree of optimality both in service allocation and migration when considering dense urban environments.

REAL TIME GRAPHICS AND VIRTUAL REALITY FOR DRIVING SIMULATION IN URBAN ENVIRONMENTS

1994 ◽  
Author(s):  
SALVADOR BAYARRI ◽  
MARCOS FERNANDEZ ◽  
MARIANO PEREZ ◽  
FRANCISCO ROSICH
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Urban Environments ◽  
Driving Simulation

scholarly journals Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems

2019 ◽  
Vol 9 (18) ◽  
pp. 3885 ◽  
Author(s):  
Bruno da Silva ◽  
Axel W. Happi ◽  
An Braeken ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Urban Environments ◽  
Machine Learning Techniques ◽  
Embedded Devices ◽  
Sound Classification ◽  
Learning Techniques ◽  
Urban Monitoring ◽  
Constrained Devices

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.

scholarly journals Site-Specific Unmodeled Error Mitigation for GNSS Positioning in Urban Environments Using a Real-Time Adaptive Weighting Model

2018 ◽  
Vol 10 (7) ◽  
pp. 1157 ◽  
Author(s):  
Zhetao Zhang ◽  
Bofeng Li ◽  
Yunzhong Shen ◽  
Yang Gao ◽  
Miaomiao Wang
Keyword(s):  
Real Time ◽  
Weighting Function ◽  
Density Ratio ◽  
Signal Propagation ◽  
Urban Environments ◽  
Equal Weight ◽  
Noise Power ◽  
Site Specific ◽  
Gnss Positioning ◽  
Adaptive Weighting

In Global Navigation Satellite System (GNSS) positioning, observation precisions are frequently impacted by the site-specific unmodeled errors, especially for the code observations that are widely used by smart phones and vehicles in urban environments. The site-specific unmodeled errors mainly refer to the multipath and other space loss caused by the signal propagation (e.g., non-line-of-sight reception). As usual, the observation precisions are estimated by the weighting function in a stochastic model. Only once the realistic weighting function is applied can we obtain the precise positioning results. Unfortunately, the existing weighting schemes do not fully take these site-specific unmodeled effects into account. Specifically, the traditional weighting models indirectly and partly reflect, or even simply ignore, these unmodeled effects. In this paper, we propose a real-time adaptive weighting model to mitigate the site-specific unmodeled errors of code observations. This unmodeled-error-weighted model takes full advantages of satellite elevation angle and carrier-to-noise power density ratio (C/N0). In detail, elevation is taken as a fundamental part of the proposed model, then C/N0 is applied to estimate the precision of site-specific unmodeled errors. The principle of the second part is that the measured C/N0 will deviate from the nominal values when the signal distortions are severe. Specifically, the template functions of C/N0 and its precision, which can estimate the nominal values, are applied to adaptively adjust the precision of site-specific unmodeled errors. The proposed method is tested in single-point positioning (SPP) and code real-time differenced (RTD) positioning by static and kinematic datasets. Results indicate that the adaptive model is superior to the equal-weight, elevation and C/N0 models. Compared with these traditional approaches, the accuracy of SPP and RTD solutions are improved by 35.1% and 17.6% on average in the dense high-rise building group, as well as 11.4% and 11.9% on average in the urban-forested area. This demonstrates the benefit to code-based positioning brought by a real-time adaptive weighting model as it can mitigate the impacts of site-specific unmodeled errors and improve the positioning accuracy.

scholarly journals FFAU—Framework for Fully Autonomous UAVs

2020 ◽  
Vol 12 (21) ◽  
pp. 3533
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
Fábio Azevedo ◽  
Ricardo Sacoto-Martins ◽  
Luís Bernardo ◽  
...  
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
New Technology ◽  
Experimental Tests ◽  
Urban Environments ◽  
Novel Approach ◽  
Safety Risks ◽  
Stationary Objects ◽  
Safety And Reliability ◽  
Remote Clients

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.

scholarly journals An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7265
Author(s):  
Zhitao Lyu ◽  
Yang Gao
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Urban Environments ◽  
Least Square ◽  
Single Frequency ◽  
Line Of Sight ◽  
Support Vector ◽  
Positioning Accuracy ◽  
Gnss Receiver ◽  
Gnss Positioning

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

The Autotrix: Design and Implementation of an Autonomous Urban Driving System

2011 ◽  
Vol 403-408 ◽  
pp. 3884-3891
Author(s):  
Animesh Garg ◽  
Anju Toor ◽  
Sahil Thakkar ◽  
Shiwangi Goel ◽  
Sachin Maheshwari ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Situational Awareness ◽  
Continuous Mapping ◽  
Digital Camera ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Multiple Sources ◽  
Time Data ◽  
Autonomous Guided Vehicle

The Autotrix is an interactive, intelligent, Autonomous Guided Vehicle (AGV) designed to serve in urban environments. Autonomous ground vehicle navigation requires the integration of many technologies such as path planning, odometry, control, obstacle avoidance and situational awareness. The objective of this project is for this prototype to navigate autonomously in an urban environment and reach its destination while detecting and avoiding obstacles on the path .This will be achieved by extracting information from multiple sources of real-time data including digital camera, GPS &ultra sonic sensors, collecting data from this extracted information, processing this data and send controlling instructions to our platform (Autotrix). The significance of this work is in presenting the methods needed for real time navigation; GPS based continuous mapping and obstacle avoidance for intelligent autonomous driving systems.

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