| 1. |
- Almeida, Tiago Rodrigues de, 1996-, et al.
(författare)
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Context-free Self-Conditioned GAN for Trajectory Forecasting
- 2022
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Ingår i: 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022. - : IEEE. - 9781665462839 ; , s. 1218-1223
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.
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| 2. |
- Almeida, Tiago Rodrigues de, 1996-, et al.
(författare)
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Trajectory Prediction for Heterogeneous Agents : A Performance Analysis on Small and Imbalanced Datasets
- 2024
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 9:7, s. 6576-6583
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Tidskriftsartikel (refereegranskat)abstract
- Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to reduce forecast uncertainty and get more accurate predictions for heterogeneous agents. However, this is hardly explored in the prior art, especially for mobile robots and in limited data applications. In this paper, we analyse different class-conditioned trajectory prediction methods on two datasets. We propose a set of conditional pattern-based and efficient deep learning-based baselines, and evaluate their performance on robotics and outdoors datasets (TH & Ouml;R-MAGNI and Stanford Drone Dataset). Our experiments show that all methods improve accuracy in most of the settings when considering class labels. More importantly, we observe that there are significant differences when learning from imbalanced datasets, or in new environments where sufficient data is not available. In particular, we find that deep learning methods perform better on balanced datasets, but in applications with limited data, e.g., cold start of a robot in a new environment, or imbalanced classes, pattern-based methods may be preferable.
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| 3. |
- Gutiérrez Maestro, Eduardo, 1994-, et al.
(författare)
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Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.
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| 4. |
- Rodrigues de Almeida, Tiago, 1996-, et al.
(författare)
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Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction
- 2023
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 24-28 Sept. 2023. - : IEEE. - 9798350399479 - 9798350399462 ; , s. 1269-1276
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Konferensbidrag (refereegranskat)abstract
- Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.
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| 5. |
- Schreiter, Tim, 1997-, et al.
(författare)
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The Magni Human Motion Dataset : Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
- 2022
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Konferensbidrag (refereegranskat)abstract
- Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.
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