Percorrer por autor "Cardoso, Pedro"
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- A computer vision based web application for tracking soccer playersPublication . Rodrigues, J. M. F.; Cardoso, Pedro; Vilas, Tiago; Mendes Da Silva, Bruno; Rodrigues, Pedro; Belguinha, António; Gomes, CarlosSoccer is a sport where everyone that is involved with it make all the efforts aiming for excellence. Not only the players need to show their skills on the pitch but also the coach, and the remaining staff, need to have their own tools so that they can perform at higher levels. Footdata is a project to build a new web application product for soccer (football), which integrates two fundamental components of this sport's world: the social and the professional. While the former is an enhanced social platform for soccer professionals and fans, the later can be considered as a Soccer Resource Planning, featuring a system for acquisition and processing information to meet all the soccer management needs. In this paper we focus only in a specific module of the professional component. We will describe the section of the web application that allows to analyse movements and tactics of the players using images directly taken from the pitch or from videos, we will show that it is possible to draw players and ball movements in a web application and detect if those movements occur during a game. © 2014 Springer International Publishing.
- Affective computing databases: in-depth analysis of systematic reviews and surveysPublication . Migueis Vaz Martins, Pedro Jorge; Rodrigues, Joao; Cardoso, PedroThe field of affective computing (AffC) is a hot research topic, where keeping track of the latest state-of-the-art can be cumbersome. Probably, due to this, a huge increase in publications of systematic reviews or surveys (SRoS) is appearing in different journals, covering various aspects such as databases, methods, and overall perspectives. Nevertheless, this increase does not mean more and better information, or at least a clarification of information. The present study analyses 10 SRoS, all published within the last 4 years, focusing only on covering AffC databases, with emphasis on collections where emotion or sentiment can be extracted from the body. It was observed that, depending on the SRoS, different information was presented, sometimes with missing or discrepant data, due to lack of information or by the way it was interpreted. As a result, from those 10 SRoS, a total of 111 different databases were analyzed, which were segmented into three groups (tiers, i.e., citation-based categorization) by their relative importance of appearance in the SRoS. In addition, it is proposed a taxonomy with a minimum set of characterizing information that researchers should address when publishing or reviewing databases.
- Affective computing emotional body gesture recognition: evolution and the cream of the cropPublication . Migueis Vaz Martins, Pedro Jorge; Rodrigues, Joao; Cardoso, PedroThe field of affective computing (AffC) has experienced significant growth, making it challenging to stay up to date with the latest advancements. This surge in interest has likely contributed to a significant rise in the number of systematic reviews or surveys (SRoS) being published across various journals, covering topics like databases, methods, and general perspectives. This paper provides three key contributions: 1) A comprehensive analysis of the evolution of emotion recognition methods from 2002 to 2024, with particular emphasis on emotional body gesture recognition, documenting a clear transition from traditional machine learning to sophisticated deep learning architectures; 2) Identification and detailed analysis of the most impactful papers (the ‘‘cream of the crop’’) that have shaped body-based AffC methods, revealing that modern approaches increasingly use attention mechanisms, graph-based representations for skeletal data, and advanced spatial-temporal modeling techniques; and 3) A systematic categorization and analysis of emotion recognition methods across architectural types (machine learning, deep learning, and hybrid) and modalities (emotional body gesture recognition, facial emotion recognition, multimodal emotion recognition, and speech emotion recognition), demonstrating the field’s progression from unimodal to more robust multimodal approaches. Through an analysis of 10 selected SRoS papers published between 2021-2024, referencing 292 papers collectively, this study reveals critical challenges including limited availability of large-scale body-based emotional databases, computational demands of modern architectures, and cross-database generalization issues.
- Air pollution forecasting using autoencoders: A classification-based prediction of NO2, PM10, and SO2 concentrationsPublication . Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Cubillas Fernández, Paloma Rocío; Ribeiro, Conceição; Cardoso, Pedro; Turias, Ignacio. J.This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values.
- Air pollution forecasting using autoencoders: a classification-based prediction of NO2, PM10, and SO2 concentrationsPublication . Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Fernández, Paloma Rocío Cubillas; Ribeiro, Conceição; Cardoso, Pedro; Turias, Ignacio. J.This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values.
- Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithmsPublication . Mendes, Tomás; Cardoso, Pedro; Monteiro, Jânio; Raposo, JoãoBuildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.
- Artificial intelligence applications and innovations: day-to-day life impactPublication . Rodrigues, João; Cardoso, Pedro; Chinnici, MartaThe idea of an intelligent machine has fascinated humans for centuries. But what is intelligence? Some define it as the capacity for learning, reasoning, understanding or, from a different perspective, the aptitude to grasp truths, relationships, facts, or meanings. All these perspectives require the capacity to acquire data from the surrounding world and, possibly, act over that environment. In short, the building of more or less autonomous agents, served with sensors and actuators, capable of learning and producing educated answers has been long foreseen. New trends in intelligente systems comprise, among other aspects, pervasive robotization, ubiquitous online data access, empowered edge computing, smart spaces, and digital ethics. These trends build the research on “Artificial Intelligence Applications and Innovation”, impacting our day-to-day life, our cities, and even our free time. Nevertheless, artificial intelligence (AI) is still closely associated with some popular misconceptions that cause the public to either have unrealistic fears about it or to have unrealistic expectations about how it will change our workplace and life in general. It is important to show that such fears are unfounded and that new trends, innovations, technologies, and smart systems will be able to improve the way we live, benefiting society without replacing humans in their core activities.
- Continual learning for object classification: integrating AutoML for binary classification tasks within a modular dynamic architecturePublication . Turner, Daniel; Cardoso, Pedro; Rodrigues, JoaoFor humans it is quite easy to identify a new object after learning to identify existing ones, but not for a machine. Deep neural networks (DNN) are the foundation of the current state-of-the-art methods for training machines to recognize sets of objects. The issue is that any modification to the DNN weights that were trained to classify an initial set of objects has the potential to seriously impair the network’s ability to make those initial classifications; this behaviour is referred to as catastrophic forgetting (CF). This paper presents a continual learning (CL) architecture that can deal with CF. The architecture is composed of two primary parts: (i) The feature extraction component, which is based on the ResNet50 backbone and (ii) the modular dynamic classification (MDC) component. The latter is made up of multiple sub-networks that gradually assemble themselves into a tree-like structure that reorganizes itself as it learns over time, so that each sub-network can operate independently. The MDC relies heavily on binary classification, and here the application of automated machine learning (AutoML) was introduced, where each binary classifier is tailored on-the-fly, and is/can be different from object to object. The strategy involves a calculated selection from a predefined list of model types and parameters, optimizing them for their respective tasks. Results demonstrate that we advanced the adaptability and performance of the network, emphasizing the transformative potential of AutoML in modular CL approaches. Tests on the CORe50 dataset showed accuracy results of 81.1%, which are above the state of the art for CL architectures.
- Cultural heritage visits supported on visitors' preferences and mobile devicesPublication . Cardoso, Pedro; Rodrigues, Joao; Pereira, Joao; Nogin, Sergey; Lessa, Joana; Ramos, Celia; Bajireanu, Roman; Gomes, Miguel; Bica, PauloMonuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general users
- A decision-support system to Analyse Customer Satisfaction Applied to a Tourism Transport ServicePublication . Ramos, Celia; Cardoso, Pedro; Fernandes, Hortênsio C. L.; Rodrigues, JoãoDue to the perishable nature of tourist products, which impacts supply and demand, the possibility of analysing the relationship between customers’ satisfaction and service quality can contribute to increased revenues. Machine learning techniques allow the analysis of how these services can be improved or developed and how to reach new markets, and look for the emergence of ideas to innovate and improve interaction with the customer. This paper presents a decision-support system for analysing consumer satisfaction, based on consumer feedback from the customer’s experience when transported by a transfer company, in the present case working in the Algarve region, Portugal. The results show how tourists perceive the service and which factors influence their level of satisfaction and sentiment. One of the results revealed that the first impression associated with good news is what creates the most value in the experience, i.e., “first impressions matter”..
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