The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Hand gesture recognition with 3D convolutional neural networks
Abstract
Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers' hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatio-temporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.
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Cites
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Cited by
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
“NAG has been used to train CNN-based models in many tasks [89]–[91].”
MethodA Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Soli: ubiquitous gesture sensing with millimeter wave radar
Lien, J., Gillian, N., Karagozler, M. E., Amihood, P., Schwesig, C., Olson, E., Raja, H., & Poupyrev, I. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925953 · Cited by 957
A review of hand gesture and sign language recognition techniques
Cheok, M. J., Omar, Z., & Jaward, M. H. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0705-5 · Cited by 600
Survey on Emotional Body Gesture Recognition
Noroozi, F., Corneanu, C. A., Kaminska, D., Sapinski, T., Escalera, S., & Anbarjafari, G. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2874986 · Cited by 455
Metadata
- Authors
- Molchanov, P., Gupta, S., Kim, K., & Kautz, J.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1-7
- OpenAlex
- W1943054406
- Cited by
- 543
- References
- 23
- FWCI
- 31.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015). Hand gesture recognition with 3D convolutional neural networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2015.7301342
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