3DInAction · Understanding human action from unstructured 3D point clouds using deep learning methods
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-01-01 → 2023-12-31
- EU contribution
- €276,205
- Participants
- 2
- Scheme
- MSCA-IF
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Results in brief
Understanding human action from unstructured 3D point clouds using deep learning methods
In this research, we address the problem of human action recognition and understanding, which is crucial in many autonomous robotic systems and other engineering problems. These systems require the ability to accurately recognize and forecast human actions, which is typically achieved using video data and deep learning methods. However, this research aims to improve upon these methods by using 3D data, specifically 3D point clouds, to improve the accuracy and robustness of human action recognition. The use of 3D data is important because it allows for a more accurate representation of human actions in the real world, as it takes into account the spatial changes that can occur in 3D environments. The overall objectives of this research are to devise novel algorithms for 3D human action recognition and forecasting using deep learning, to create an annotated dataset for training and testing these algorithms, and to suggest new methods for tackling the challenges of 3D human action recognition. The resulting algorithms have the potential to be used in a wide range of scientific and engineering applications, such as human-robot interaction.
Data: CORDIS, © European Union
Project objective
Human action recognition and forecasting is an integral part of autonomous robotic systems that require human-robot interaction as well as other engineering problems. Action recognition is typically achieved using video data and deep learning methods. However, other tasks, e.g. classification, showed that it is often beneficial to additionally use 3D data. Namely, 3D point clouds that are sampled on the surfaces of objects and agents in the scene. Unfortunately, existing human action recognition methods are somewhat limited, motivating the following research. In this action, we describe a new class of algorithms for 3D human action recognition and forecasting using a deep learning-based approach. Our approach is novel in that it extends a recent body of work on action recognition from 2D to the 3D domain which is particularly challenging due to the unstructured, unordered and permutation invariant nature of 3D point clouds. Our algorithms use the global and local statistical properties of 3D point clouds along with a 3D convolutional neural network to devise novel multi-modal representation of human action. It is inherently robust to spatial changes in the 3D domain, unlike previous works which rely on the 2D projections. In practice, deep learning methods allow us to learn an inference model from real-world examples. A common methodology for action recognition includes creating an annotated dataset, training an inference model and testing its generalization. Our research objectives cover all of these tasks and suggest novel methods to tackle them. Overall, the proposed research offers a new point of view for these long-standing problems, and with the vast related work in other domains, it may bridge the gap to arrive at a generalizable, effective and efficient 3D human action recognition and forecasting machinery. The resulting algorithms may be used in several scientific and engineering domains such as human-robot interaction among other applications.
Original text from CORDIS.
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Data: CORDIS, © European Union