A Multi-Stage Clustering Framework for Automotive Radar Data

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.Comment: 8 pages, 5 figures, accepted paper for 2019 IEEE 22nd Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, October 201

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Aprendizagem automática aplicada à deteção de pessoas baseada em radar

The present dissertation describes the development and implementation of a radar-based system with the purpose of being able to detect people amidst other objects that are moving in an indoor scenario. The detection methods implemented exploit radar data that is processed by a system that includes the data acquisition, the pre-processing of the data, the feature extraction, and the application of these data to machine learning models specifically designed to attain the objective of target classification. Beyond the basic theoretical research necessary for its sucessful development, the work contamplates an important component of software development and experimental tests. Among others, the following topics were covered in this dissertation: the study of radar working principles and hardware; radar signal processing; techniques of clutter removal, feature exctraction, and data clustering applied to radar signals; implementation and hyperparameter tuning of machine learning classification systems; study of multi-target detection and tracking methods. The people detection application was tested in different indoor scenarios that include a static radar and a radar dynamically deployed by a mobile robot. This application can be executed in real time and perform multiple target detection and classification using basic clustering and tracking algorithms. A study of the effects of the detection of multiple targets in the performance of the application, as well as an assessment of the efficiency of the different classification methods is presented. The envisaged applications of the proposed detection system include intrusion detection in indoor environments and acquisition of anonymized data for people tracking and counting in public spaces such as hospitals and schools.A presente dissertação descreve o desenvolvimento e implementação de um sistema baseado em radar que tem como objetivo detetar e distinguir pessoas de outros objetos que se movem num ambiente interior. Os métodos de deteção e distinção exploram os dados de radar que são processados por um sistema que abrange a aquisição e pré-processamento dos dados, a extração de características, e a aplicação desses dados a modelos de aprendizagem automática especificamente desenhados para atingir o objetivo de classificação de alvos. Além do estudo da teoria básica de radar para o desenvolvimento bem sucedido desta dissertação, este trabalho contempla uma componente importante de desenvolvimento de software e testes experimentais. Entre outros, os seguintes tópicos foram abordados nesta dissertação: o estudo dos princípios básicos do funcionamento do radar e do seu equipamento; processamento de sinal do radar; técnicas de remoção de ruído, extração de características, e segmentação de dados aplicada ao sinal de radar; implementação e calibração de hiper-parâmetros dos modelos de aprendizagem automática para sistemas de classificação; estudo de métodos de deteção e seguimento de múltiplos alvos. A aplicação para deteção de pessoas foi testada em diferentes cenários interiores que incluem o radar estático ou transportado por um robot móvel. Esta aplicação pode ser executada em tempo real e realizar deteção e classificação de múltiplos alvos usando algoritmos básicos de segmentação e seguimento. O estudo do impacto da deteção de múltiplos alvos no funcionamento da aplicação é apresentado, bem como a avaliação da eficiência dos diferentes métodos de classificação usados. As possíveis aplicações do sistema de deteção proposto incluem a deteção de intrusão em ambientes interiores e aquisição de dados anónimos para seguimento e contagem de pessoas em espaços públicos tais como hospitais ou escolas.Mestrado em Engenharia de Computadores e Telemátic

Temporal Cluster Analysis for radar satellite data

EnClustering is a popular technique of data analysis and data mining. Since clustering problems are complex in nature, the larger is the size of the problem, the harder is to find the optimal solution and the longer it takes to reach reasonable results. Clustering techniques are conventionally divided in hierarchical and partitioning. In this paper I present a review of the clustering algorithms for large temporal databases and an application to radar satellite data in which I study different types of ground deformation trend by SAR images of the European Space Agency. The studied region is the area between the cities of Benevento and Avellino