The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks
Abstract
Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult, 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification, in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83:8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88:4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.
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Cites
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
Improving neural networks by preventing co-adaptation of feature detectors
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Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
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Response time in man-computer conversational transactions
Miller, R. B. (1968) · Proceedings of the December 9-11, 1968, fall joint computer conference, part I on - AFIPS '68 (Fall, part I) · doi:10.1145/1476589.1476628 · Cited by 942
ModDrop: Adaptive Multi-Modal Gesture Recognition
Neverova, N., Wolf, C., Taylor, G., & Nebout, F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2461544 · Cited by 420
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
ChaLearn Looking at People Challenge 2014: Dataset and Results
Escalera, S., Baró, X., Gonzàlez, J., Bautista, M. A., Madadi, M., Reyes, M., Ponce-López, V., Escalante, H. J., Shotton, J., & Guyon, I. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16178-5_32 · Cited by 211
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
Cited by
A 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
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
Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition
Huang, J., Zhou, W., Li, H., & Li, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870740 · Cited by 232
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
Metadata
- Authors
- Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., & Kautz, J.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4207-4215
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2471695703
- Cited by
- 695
- References
- 9
- FWCI
- 41.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., & Kautz, J. (2016). Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.456
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