Projects per year
We address an actively discussed problem in signal processing, recognizing patterns from spatial data in motion. In particular, we suggest a neural network architecture to recognize motion patterns from 4D point clouds. We demonstrate the feasibility of our approach with point cloud datasets of hand gestures. The architecture, PointGest, directly feeds on unprocessed timelines of point cloud data without any need for voxelization or projection. The model is resilient to noise in the input point cloud through abstraction to lower-density representations, especially for regions of high density. We evaluate the architecture on a benchmark dataset with ten gestures. PointGest achieves an accuracy of 98.8%, outperforming five state-of-the-art point cloud classification models.
|Title of host publication||Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020|
|Number of pages||6|
|Publication status||Published - Sep 2020|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE International Workshop on Machine Learning for Signal Processing - Aalto University, Espoo, Finland|
Duration: 21 Sep 2020 → 24 Sep 2020
Conference number: 30
|Name||IEEE International Workshop on Machine Learning for Signal Processing|
|Workshop||IEEE International Workshop on Machine Learning for Signal Processing|
|Period||21/09/2020 → 24/09/2020|
- 4D point clouds
- Deep learning
- Gesture recognition
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WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning
Tirkkonen, O., Salami, D., Sigg, S. & Kazemi, P.
01/01/2019 → 30/06/2023
Project: EU: MC
RadioSense: Wireless Big Data Augmented Smart Industry
Sigg, S., Zuo, S., Naas, S., Kodali, M., Raja, M. & Palipana, S.
01/01/2019 → 28/02/2022
Project: Academy of Finland: Other research funding