About
Areas of research:
- Knowledge discovery
- Supervised learning
- Multiple predictive models
- Applied knowledge discovery
- Intelligent transportation systems
- Planning and operations of public transports
Areas of research: - Knowledge discovery Supervised learning Multiple predictive models Applied knowledge discovery - Intelligent transportation systems Planning and operations of public transports
Areas of research:
- Knowledge discovery
- Intelligent transportation systems
2023
Authors
Mendes-Neves, T; Seca, D; Sousa, R; Ribeiro, C; Mendes-Moreira, J;
Publication
Computational Economics
Abstract
2022
Authors
Bhanu, M; Kumar, R; Roy, S; Mendes-Moreira, J; Chandra, J;
Publication
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2022, PT I
Abstract
Capturing complex spatio-temporal features of thousands of correlated taxi-demand time-series in the city makes the traffic flow prediction problem a challenging task. Hence, several Deep Neural Network (DNN) models have been developed to mimic the latent spatio-temporal behaviour of taxi-demand time-series in a city to improve the prediction results. Despite, good performance of recent DNN based traffic prediction techniques, such models can only identify either adjacent or connected regions with direct or transitive connection; hence they fail to capture spatio-temporal correlation among regions that exhibit implicit or latent connection. Additionally, the dependency of the recent DNN models on recursive components facilitates error propagation during feature aggregation without any counter strategy for it. In view of these existing glitches, we introduce a novel DNN model, graph Multi-Head Convolution for Spatio-Temporal Aggregation (gMHC-STA) which supports capturing spatio-temporal correlation among regions with explicit and implicit connection both. Moreover, gMHC-STA aggregates both spatial and temporal characteristics using multi-head attention; thus overriding recursive RNN or its variant approach to prevent noise propagation. The experimental results of gMHC-STA on two real-world city taxi-demand datasets report minimum of 6.5–10% improvement over the best state-of-the-art on standard benchmark metric in varying experimental conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2022
Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;
Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022
Abstract
2022
Authors
Strecht, P; Mendes Moreira, J; Soares, C;
Publication
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2022, PT II
Abstract
2022
Authors
Couceiro, M; Lima, IR; Ulisses, A; Neves, TM; Moreira, JM;
Publication
Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support, icSPORTS 2022, Valletta, Malta, October 27-28, 2022.
Abstract
The broadcast of audio-video sports content is a field with increasingly larger audiences demanding higher quality content and involvement. This growth creates the necessity to develop more content to engage the users and keep this trend. Otherwise, it may stall or even diminish. Therefore, enhancing the user experience, engagement, and involvement during live sports event broadcasts is of utmost importance. This paper proposes a solution to extract event’s information from video, resorting to Computer Vision techniques and Deep Learning algorithms. More specifically, the project encompassed the definition and implementation of field registration, object detection and tracking tasks. Focusing on football sports events, a novel dataset combining several video sources was created and used for analysis and metadata extraction. In particular, the proposed solution can detect and track players with acceptable precision using state-of-the-art methods, like YOLOv5 and DeepSORT. Furthermore, resorting to unsupervised learning techniques, the system provides team segmentation based on the colour of the players’ kits. A series of visual representations regarding the players’ movements on the field enables broadcast enrichment and increased user experience. The presented solution is framed in the H2020 DataCloud project and will be deployed in a cloud environment simplifying its access and utilisation. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Supervised Thesis
2022
Author
Maria José Gomes Pedroto
Institution
UP-FEUP
2022
Author
Duarte Miguel de Novo Faria
Institution
UP-FEUP
2022
Author
Paulo Jorge Silva Ferreira
Institution
UP-FEUP
2022
Author
Murilo de Mendonça Couceiro
Institution
UP-FEUP
2022
Author
Akilu Rilwan Muhammad
Institution
UP-FEUP
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