1 |
ZHAO J B, ALLISON R S. Real‑time head gesture recognition on head‑mounted displays using cascaded hidden Markov models[A]. Proceedings of the 2017 IEEE International Conference on Systems, Man and Cybernetics[C]. Banff, AB,Canada: IEEE, 2017. 2361 - 2366.
|
2 |
VADIRAJ S K, RAO A, GHOSH P K. Automatic identification of speakers from head gestures in a narration[A]. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing[C]. Barcelona, Spain: IEEE, 2020. 6314 - 6318.
|
3 |
SHARMA M, AHMETOVIC D, JENI L A, et al. Recognizing visual signatures of spontaneous head gestures[A]. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision[C]. Nevada,USA: IEEE, 2018. 400 - 408.
|
4 |
ZHAO J B, ALLISON R S. Comparing head gesture hand gesture and gamepad interfaces for answering Yes/No questions in virtual environments[J]. Virtual Reality, 2020, 24: 515 - 524.
|
5 |
FUJIE S, EJIRI Y, NAKAJIMA K, et al. A conversation robot using head gesture recognition as para‑linguistic information[A]. Proceedings of the RO‑MAN 2004 13th IEEE International Workshop on Robot and Human Interactive Communication[C]. Kurashiki, Japan: IEEE, 2004. 159 - 164.
|
6 |
NG P C, SILVA L C D. Head gestures recognition[A]. Proceedings of the 2001 International Conference on Image Processing[C]. Thessaloniki, Greece: IEEE, 2001. 266 - 269.
|
7 |
SOLEA R, MARGARIT A, CERNEGA D, et al. Head movement control of powered wheelchair[A]. Proceedings of the 23rd International Conference on System Theory, Control and Computing [C]. Sinaia, Romania: IEEE, 2019. 632 - 637.
|
8 |
JACKOWSKI A, GEBHARD M, THIETJE R. Head motion and head gesture‑based robot control: A usability study[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(1): 161 - 170.
|
9 |
WU C W, YANG H Z, CHEN Y A, et al. Applying machine learning to head gesture recognition using wearables[A]. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology[C]. Taichung, China: IEEE, 2017. 436 - 440.
|
10 |
JACKOWSKI A, GEBHARD M, GRÄSER A. A novel head gesture based interface for hands‑free control of a robot[A]. Proceedings of the 2016 IEEE International Symposium on Medical Measurements and Applications[C]. Benevento,Italy: IEEE, 2016. 1 - 6.
|
11 |
RUDIGKEIT N, GEBHARD M, GRÄSER A. An analytical approach for head gesture recognition with motion sensors[A]. Proceedings of the 2015 9th International Conference on Sensing Technology[C]. Auckland, New Zealand: IEEE, 2015. 1 - 6.
|
12 |
BANKAR R, SALANKAR S. Improvement of head gesture recognition using Camshift based face tracking with UKF[A]. Proceedings of the 2019 9th International Conference on Emerging Trends in Engineering and Tech‑ nology‑Signal and Information Processing[C]. Nagpur, India: IEEE, 2019. 1 - 5.
|
13 |
LU P, ZHANG M, ZHU X, et al. Head nod and shake recognition based on multi‑view model and hidden Markov model[A]. Proceedings of the International Conference on Computer Graphics, Imaging and Visualization[C]. Beijing, China: IEEE, 2005. 61 - 64.
|
14 |
HONG T, LI Y W, WANG Z Y. Real‑time head action recognition based on HOF and ELM[J]. IEICE Transactions on Information and Systems, 2019, 102(1): 206 - 209.
|
15 |
罗会兰, 童康, 孔繁胜. 基于深度学习的视频中人体动作识别进展综述[J]. 电子学报, 2019, 47(5): 1162 - 1173.
|
|
LUO Hui‑lan, TONG Kang, KONG Fan‑sheng. The progress of human action recognition in videos based on deep learning: A review[J]. Acta Electronica Sinica, 2019, 47(5): 1162 - 1173.(in Chinese)
|
16 |
FARNEBÄCK G. Two‑frame motion estimation based on polynomial expansion[A]. Proceedings of the 13th Scandinavian Conference on Image Analysis[C]. Halmstad, Sweden: Springer, 2003. 363 - 370.
|
17 |
LECUN Y, HUANG F J, BOTTOU L. Learning methods for generic object recognition with invariance to pose and lighting[A]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. Washington,USA: IEEE, 2004. 97 - 104.
|
18 |
SIMONYAN K, ZISSERMAN A. Two‑stream convolutional networks for action recognition in videos[J]. Advances in Neural Information Processing Systems, 2014, 27: 568 - 576.
|
19 |
JI S, XU W, YANG M, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1): 221 - 231.
|
20 |
TRAN D, BOUTDEV L, FERGUS R, et al. Learning spatiotemporal features with 3D convolutional networks[A]. Proceedings of the 2015 IEEE International Conference on Computer Vision[C]. Santiago,Chile: IEEE, 2015. 4489 - 4497.
|
21 |
FEICHTENHOFER C, PINZ A, ZISSERMAN A. Convolutional two‑stream network fusion for video action recognition[A]. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition[C]. Las Vegas, USA: IEEE, 2016. 1933 - 1941.
|
22 |
NAIR V, HINTON G E. Rectified linear units improve restricted Boltzmann machines[A]. Proceedings of the 27th International Conference on Machine Learning[C]. Haifa, Israel: ACM, 2010. 807 - 814.
|
23 |
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929 - 1958.
|
24 |
KINGMA D P, Ba J. Adam: A method for stochastic optimization[A]. Proceedings of the 3rd International Conference on Learning Representations[C]. San Diego, USA: Conference Track Proceedings, 2015. 1884 - 2021.
|
25 |
SUNI S S, GOPAKUMAR K. A real time decision support system using head nod and shake[A]. Proceedings of the 2016 International Conference on Circuit, Power and Computing Technologies[C]. Nagercoil, India: IEEE, 2016. 1 - 5.
|