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Semantic Deep Learning to Translate Dynamic Sign Language
2021
International Journal of Intelligent Engineering and Systems
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We used Three-dimensional Convolutional Neural Networks followed by Convolutional long short-term memory to improve the recognition accuracy in Dynamic sign language recognition. ...
Real-life video gesture frames couldn't be treated as frame-level as a static sign. This research proposes a semantic translation system for dynamic hand gestures using deep learning and ontology. ...
Elsayed; writing-review and editing, Eman K. Elsayed and Doaa R. Fathy; supervised the study, analyzed the results, and verified the findings of the study. ...
doi:10.22266/ijies2021.0228.30
fatcat:hqlobptq65hu7azhhhrfwqaucy
3D Skeletal Joints-Based Hand Gesture Spotting and Classification
2021
Applied Sciences
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This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. ...
Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module identifies the segmented gestures. ...
And classification of hand gestures used only fundamental 3DCNN networks without employing the LSTM network. ...
doi:10.3390/app11104689
fatcat:cvee5keiubbhjjvctp5rsgthzy
Large-Scale Multimodal Gesture Segmentation and Recognition Based on Convolutional Neural Networks
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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In the segmentation module, a continuous gesture sequence is segmented into isolated gesture sequences by classifying the frames into gesture frames and transitional frames using two stream convolutional ...
This paper presents an effective method for continuous gesture recognition. The method consists of two modules: segmentation and recognition. ...
Acknowledgment Huogen Wang and Pichao Wang gratefully acknowledge the financial support from China Scholarship Council. ...
doi:10.1109/iccvw.2017.371
dblp:conf/iccvw/WangWSL17a
fatcat:edlsxxugb5dbpdu54bi7prgqyq
Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model
2019
Sensors
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Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. ...
Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. ...
Acknowledgments: We thank the research project "A Deep Learning-Based Gesture Interface and Value-Added Location Services" sponsored by the Ministry of Science and Technology, Taiwan. ...
doi:10.3390/s19245429
pmid:31835404
pmcid:PMC6961023
fatcat:27f7jh5nlnaufchz6scmg7cq3m
Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms
2021
International Journal of Interactive Multimedia and Artificial Intelligence
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Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint ...
Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results. ...
Acknowledgment We are grateful to the College of Engineering Roorkee, India, and UTU Dehradun, India, for providing excellent research facility to carry out this research work. ...
doi:10.9781/ijimai.2021.08.008
fatcat:qyaa7v57hncabkrk37ustuo5fy
Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has ...
The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. ...
doi:10.1109/iccvw.2017.377
dblp:conf/iccvw/WanEAEBGMAGLX17
fatcat:5v7hbhcbqjawrbs7t7erfxzx3i
Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation
2020
IEEE Access
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In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature ...
However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence ...
Recently, deep neural network architectures, such as CNN and long short-term memory (LSTM) network, have been used for hand gesture recognition. ...
doi:10.1109/access.2020.3032140
fatcat:x2h4fd5csjhgxhbnjynmmct64q
Large-Scale Multimodal Gesture Recognition Using Heterogeneous Networks
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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rank pooling and adopts Convolutional LSTM Networks (Con-vLSTM) to learn long-term spatiotemporal features from short-term spatiotemporal features extracted using a 3D convolutional neural network (3DCNN ...
The method has been evaluated on the 2017 isolated and continuous ChaLearn LAP Large-scale Gesture Recognition Challenge datasets and the results are ranked among the top performances. ...
Acknowledgment Huogen Wang and Pichao Wang gratefully acknowledge financial support from China Scholarship Council. ...
doi:10.1109/iccvw.2017.370
dblp:conf/iccvw/WangWSL17
fatcat:rvangyx55nhi7glu7a757v5k6e
Computer Control Using Vision-Based Hand Motion Recognition System
2022
ITM Web of Conferences
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In our day-to-day communication and expression, gestures play a crucial role. As a result, using them to interact with technical equipment requires small cognitive data processing on our part. ...
In this study, we created a sophisticated marker-free hand gesture detection structure that can monitor both dynamic and static hand gestures. ...
Hand detection and segmentation of related picture areas are the first steps in hand gesture recognition systems. ...
doi:10.1051/itmconf/20224403069
fatcat:bheedv45afamjljaoaurw2pv24
Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
[article]
2021
arXiv
pre-print
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Current gesture recognition methods have focused on recognising isolated gestures, and existing continuous gesture recognition methods are limited to two-stage approaches where independent models are required ...
In contrast, we introduce a single-stage continuous gesture recognition framework, called Temporal Multi-Modal Fusion (TMMF), that can detect and classify multiple gestures in a video via a single model ...
, convolutional LSTM and 2D-CNNs. ...
arXiv:2011.04945v2
fatcat:q4z7xkt22vbdjlavwkglom33gy
Table of Contents
2019
IEEE transactions on multimedia
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Tian 986 Deep Learning for Multimedia Analysis Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Jeng 887 Synthesis of Realistic Facial Expressions Using Expression Map . . . . . . . . . . . . . . . . . . . . S. Agarwal and D. P. ...
doi:10.1109/tmm.2019.2904597
fatcat:xptgvh44knfizl2tt3wsr4sgxe
A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets
[article]
2022
arXiv
pre-print
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Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. ...
Finally, we find the research gap and limitations in this domain and suggest future directions. ...
Midpoint algorithm, SSD (Sin- gle Shot Detector), 2DCNN (2D Hidden Markov Model, and kiosk. Convolutional Neural Network), 3DCNN (3D Convolutional Neural Network), and LSTM (Long Short- Term Memory). ...
arXiv:2204.03328v1
fatcat:72kb7zz5xfaqxa2l5sz22drrwi
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
[article]
2018
arXiv
pre-print
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Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. ...
In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. ...
[178] presented a gesture recognition method using C3D [130] and convolutional LSTM (convLSTM) [163] based on depth and RGB modalities (see Figure 17 ). ...
arXiv:1711.08362v2
fatcat:cugugpqeffcshnwwto4z2aw4ti
Two Stage Continuous Gesture Recognition Based on Deep Learning
2021
Electronics
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The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. ...
In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented ...
Both the temporal segmentation and the recognition problems need to be solved in continuous gesture recognition. In fact, temporal segmentation and gesture recognition can be solved separately. ...
doi:10.3390/electronics10050534
fatcat:nwnhgmfyfjgn5an2mfneu5xc4a
Sparse Deep LSTMs with Convolutional Attention for Human Action Recognition
2021
SN Computer Science
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Overfitting is reduced due to using a sparse layer instead of a dropout based on the results achieved. Moreover, a deep LSTM network leads to a higher recognition rate than one-layer LSTM. ...
In this paper, an architecture is proposed for action recognition, including ResNet feature extractor, Conv-Attention-LSTM, BiLSTM, and fully connected layers. ...
If continuous action recognition is assumed to perform, a segmentation algorithm of actions [60] is required as a pre-processing step before the proposed algorithm owing not to mixing the information ...
doi:10.1007/s42979-021-00576-x
fatcat:keuxpwjtljfatjicqc6xareyl4
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