Motion pattern recognition in 4D point clouds

Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020, IEEE International Workshop on Machine Learning for Signal Processing

Abstract

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.

Description

| openaire: EC/H2020/813999/EU//WINDMILL

Keywords

4D point clouds, Deep learning, Gesture recognition

DOI

10.1109/MLSP49062.2020.9231569

Citation

Salami, D, Palipana, S, Kodali, M & Sigg, S 2020, Motion pattern recognition in 4D point clouds. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231569, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Finland, 21/09/2020. https://doi.org/10.1109/MLSP49062.2020.9231569

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-2020123160399

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[article-cris] Sähkötekniikan korkeakoulu / ELEC

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Motion pattern recognition in 4D point clouds

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