The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM
Open accessAbstract
Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human-computer/robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02% on the validation set of IsoGD and 98.89% on SKIG).
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Pigou, L., van den Oord, A., Dieleman, S., Van Herreweghe, M., & Dambre, J. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-016-0957-7 · Cited by 290
Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network
Nishida, N., & Nakayama, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-29451-3_54 · Cited by 53
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Tang, M., Liu, Y., & Durlofsky, L. J. (2020) · Journal of Computational Physics · doi:10.1016/j.jcp.2020.109456 · Cited by 394
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
Kopuklu, O., Gunduz, A., Kose, N., & Rigoll, G. (2019) · 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) · doi:10.1109/fg.2019.8756576 · Cited by 319
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training
Abavisani, M., Joze, H. R. V., & Patel, V. M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00126 · Cited by 190
Multimodal Gesture Recognition Based on the ResC3D Network
Miao, Q., Li, Y., Ouyang, W., Ma, Z., Xu, X., Shi, W., & Cao, X. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.360 · Cited by 168
Spatio-temporal convolutional features with nested LSTM for facial expression recognition
Yu, Z., Liu, G., Liu, Q., & Deng, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.028 · Cited by 162
Metadata
- Authors
- Zhu, G., Zhang, L., Shen, P., & Song, J.
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 4517-4524
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2595328592
- Cited by
- 270
- References
- 6
- FWCI
- 45.68
- Type
- journal article
- Language
- en
- Open access
- gold
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