A Gesture Cognition Strategy for High-speed Train Drivers on Reconstructed Multiple Views

Asadi-Aghbolaghi, M., Claps, A., Bellantonio, M., Es-calante, H.J., Ponce-Lpez, V., Bar, X., Guyon, I., Kasaei, S., and Escalera, S. (2017). A survey on deep learning based approaches for action and gesture recognition in image sequences. 12th IEEE International Conference on Automatic Face Gesture Recognition, 476–483. Da Gama, 2016, Mirrarbilitation: A clinically-related gesture recognition interactive tool for an ar rehabilitation system, Computer Methods and Programs in Biomedicine, 135, 105, 10.1016/j.cmpb.2016.07.014 Duan, J., Zhou, S., Wan, J., Guo, X., and Li, S. (2016). Multi-modality fusion based on consensus-voting and 3d convolution for isolated gesture recognition. Gustin, 2016, Hand gesture recognition from multibeam sonar imagery*, IFAC-PapersOnLine, 49, 470, 10.1016/j.ifacol.2016.10.450 Kopuklu, O., Kse, N., and Rigoll, G. (2018). Motion fused frames: Data level fusion strategy for hand gesture recognition. Molchanov, P., Gupta, S., Kim, K., and Kautz, J. (2015). Hand gesture recognition with 3d convolutional neural networks. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Plouffe, 2015, Static and dynamic hand gesture recognition in depth data using dynamic time warping, IEEE Transactions on Instrumentation and Measurement, 65, 305, 10.1109/TIM.2015.2498560 Pramod, 2015, Recent methods and databases in vision-based hand gesture recognition: A review, Computer Vision and Image Understanding, 141, 152, 10.1016/j.cviu.2015.08.004 Rautaray, 2015, Vision based hand gesture recognition for human computer interaction: a survey, Artificial Intelligence Review, 43, 1, 10.1007/s10462-012-9356-9 Sharma, 2012, Depth estimation of features in video frames with improved feature matching technique using kinect sensor, Optical Engineering, 51, 1, 10.1117/1.OE.51.10.107002 Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. In 2015 IEEE International Conference on Computer Vision (ICCV), 4489–4497. Wan, J., Li, S., Zhao, Y., Zhou, S., Guyon, I., and Escalera, S. (2016). Chalearn looking at people rgb-d isolated and continuous datasets for gesture recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 761–769. Wang, 2012, Research on monitoring plan of fatigue driving status for locomotive driver, Railway Computer Application, 12, 7 Zhu, 2017, Mul-timodal gesture recognition using 3-d convolution and convolutional lstm, IEEE Access, 5, 4517, 10.1109/ACCESS.2017.2684186