Multilayer Information Management System for personalized urban pedestrian routing

The present paper aims to describe the work carried out inside the ARGUS project to design and develop a software tool that manages heterogeneous cartographical datasets in order to offer personalized routing services. The project is focused on guiding blind and visually impaired in urban and rural environments with the help of binaural sounds. The navigation algorithm in the ARGUS smartphone application relies on GPX tracks containing the path to follow and informative points of interest along the path. These GPX files, previously recorded or created on demand, are downloaded from the remote service platform where the Multilayer Information Management System is hosted. This module handles, on one hand, crowdsourced data from OpenStreetMap and ARGUS users and, on the other hand, cartography from individual city providers. Moreover the system defines a set of spatial attributes to categorize the most relevant and signifcant types of urban elements for the target user group, which are represented as geographical point or lines, enabling users to decide which type of objects have a positive effect such as tactile pavements, negative or neutral effect during the trace of a path. This user specified aproach affect the route finding by changing the routing weights. Different levels of visual impairment and skills from one user to another, as well as personal preferences, make this module a decisive configurable abstraction layer for the route calculation module

3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction

This paper presents a new approach to 3D object detection that leverages the properties of the data obtained by a LiDAR sensor. State-of-the-art detectors use neural network architectures based on assumptions valid for camera images. However, point clouds obtained from LiDAR are fundamentally different. Most detectors use shared filter kernels to extract features which do not take into account the range dependent nature of the point cloud features. To show this, different detectors are trained on two splits of the KITTI dataset: close range (objects up to 25 meters from LiDAR) and long-range. Top view images are generated from point clouds as input for the networks. Combined results outperform the baseline network trained on the full dataset with a single backbone. Additional research compares the effect of using different input features when converting the point cloud to image. The results indicate that the network focuses on the shape and structure of the objects, rather than exact values of the input. This work proposes an improvement for 3D object detectors by taking into account the properties of LiDAR point clouds over distance. Results show that training separate networks for close-range and long-range objects boosts performance for all KITTI benchmark difficulties.Comment: 10 pages, 8 figures, 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020

Efficient multi-task based facial landmark and gesture detection in monocular images

[EN] The communication between persons includes several channels to exchange information between individuals. The non-verbal communication contains valuable information about the context of the conversation and it is a key element to understand the entire interaction. The facial expressions are a representative example of this kind of non-verbal communication and a valuable element to improve human-machine interaction interfaces. Using images captured by a monocular camera, automatic facial analysis systems can extract facial expressions to improve human-machine interactions. However, there are several technical factors to consider, including possible computational limitations (e.g. autonomous robots), or data throughput (e.g. centralized computation server). Considering the possible limitations, this work presents an efficient method to detect a set of 68 facial feature points and a set of key facial gestures at the same time. The output of this method includes valuable information to understand the context of communication and improve the response of automatic human-machine interaction systems

On-demand serverless video surveillance with optimal deployment of deep neural networks

[EN] We present an approach to optimally deploy Deep Neural Networks (DNNs) in serverless cloud architectures. A serverless architecture allows running code in response to events, automatically managing the required computing resources. However, these resources have limitations in terms of execution environment (CPU only), cold starts, space, scalability, etc. These limitations hinder the deployment of DNNs, especially considering that fees are charged according to the employed resources and the computation time. Our deployment approach is comprised of multiple decoupled software layers that allow effectively managing multiple processes, such as business logic, data access, and computer vision algorithms that leverage DNN optimization techniques. Experimental results in AWS Lambda reveal its potential to build cost-effective ondemand serverless video surveillance systems.This work has been partially supported by the program ELKARTEK 2019 of the Basque Government under project AUTOLIB

Building synthetic simulated environments for configuring and training multi-camera systems for surveillance applications

[EN] Synthetic simulated environments are gaining popularity in the Deep Learning Era, as they can alleviate the effort and cost of two critical tasks to build multi-camera systems for surveillance applications: setting up the camera system to cover the use cases and generating the labeled dataset to train the required Deep Neural Networks (DNNs). However, there are no simulated environments ready to solve them for all kind of scenarios and use cases. Typically, ‘ad hoc’ environments are built, which cannot be easily applied to other contexts. In this work we present a methodology to build synthetic simulated environments with sufficient generality to be usable in different contexts, with little effort. Our methodology tackles the challenges of the appropriate parameterization of scene configurations, the strategies to generate randomly a wide and balanced range of situations of interest for training DNNs with synthetic data, and the quick image capturing from virtual cameras considering the rendering bottlenecks. We show a practical implementation example for the detection of incorrectly placed luggage in aircraft cabins, including the qualitative and quantitative analysis of the data generation process and its influence in a DNN training, and the required modifications to adapt it to other surveillance contexts.This work has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 865162, SmaCS (https://www.smacs.eu/

Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells

Significant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power

Concerns on Design and Performance of a Local and Global Dynamic Map

Current real-time data collection systems for urban transportation and mobility allow enhancing digital maps with up-to-date situational information. This information is of great interest for short-term navigation and route planning as well as for medium- to long-term mobility data analysis, as it provides a finer time-varying detail of the urban movement infrastructure. In this work, we present our ongoing work to design a representation of a unique urban movement space graph as a local and global dynamic map approach. We address the concerns that must be considered when handling different scales of geographic areas inside a city, according to the application

RTMaps-based Local Dynamic Map for multi-ADAS data fusion

Work on Local Dynamic Maps (LDM) implementation is still in its early stages, as the LDM standards only define how information shall be structured in databases, while the mechanism to fuse or link information across different layers is left undefined. A working LDM component, as a real-time database inside the vehicle is an attractive solution to multi-ADAS systems, which may feed a real-time LDM database that serves as a central point of information inside the vehicle, exposing fused and structured information to other components (e.g., decision-making systems). In this paper we describe our approach implementing a real-time LDM component using the RTMaps middleware, as a database deployed in a vehicle, but also at road-side units (RSU), making use of the three pillars that guide a successful fusion strategy: utilisation of standards (with conversions between domains), middlewares to unify multiple ADAS sources, and linkage of data via semantic concepts.Comment: 9 pages. To be published in 14th ITS European Congress 202