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ISOLATED AND CONTINUOUS HAND GESTURE RECOGNITION BASED ON DEEP LEARNING: A REVIEW
2022
Zenodo
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A review of recent studies in this field and the division of recognition systems into continuous and isolated and the algorithms used in both methods: Recurrent Neural Network (RNN) based method, Convolutional ...
Neural Network (CNN), and Three-Dimensional Convolutional Neural Network (3D-CNN), in addition to the challenges in systems, Identify the signal, problems, and prospects. ...
Data collection distinguishes deep learning-based techniques from sensor-based approaches. This section focuses on deep learning-based visionbased gesture recognition research methodologies. ...
doi:10.5281/zenodo.7353683
fatcat:4io4f6gkffbqnd3ikdcu2xte4y
Review of dynamic gesture recognition
2021
Virtual Reality & Intelligent Hardware
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videos based on deep learning. ...
The reviewed methods are broadly categorized into three groups based on the type of neural networks used for recognition: twostream convolutional neural networks, 3D convolutional neural networks, and ...
Method based on 3D convolutional neural networks initially completed a dynamic gesture recognition algorithm directly based on ResC3D [59] . ...
doi:10.1016/j.vrih.2021.05.001
fatcat:jpddnlf2xbfufnyuf3s6fbxgty
Dynamic Gesture Recognition Based on Feature Fusion Network and Variant ConvLSTM
2020
IET Image Processing
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In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. ...
Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. ...
Acknowledgments This paper is supported by the National Key Research and Development Program of China(2018YFB1306900), and National Natural Science Foundation of China (NO.U1813222). ...
doi:10.1049/iet-ipr.2019.1248
fatcat:jkoybfiaovbb3cnktsh7mbx4jm
3D Motion Gesture Control : Gesture Recognition and Adaptation for Human Computer Interaction
2024
International Journal of Applied and Advanced Multidisciplinary Research
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These adaptations can range from adjusting the interface layout to suit the user's preferences to dynamically changing the system's behavior based on the user's gestures. ...
It highlights how 3D gesture recognition can be integrated into adaptive HCI systems, enabling personalized and context-aware interactions. ...
These adaptations can range from adjusting the interface layout to suit the user's preferences to dynamically changing the system's behavior based on the user's gestures. ...
doi:10.59890/ijaamr.v2i1.730
fatcat:r3lnnxjqizdynowqwd65qridrm
Large-Scale Multimodal Gesture Recognition Using Heterogeneous Networks
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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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. ...
This paper presents the method designed for the 2017 ChaLearn LAP Large-scale Gesture Recognition Challenge. ...
Zhanjie Song was partly supported by National Natural Science Foundation of China (Grant No.61379014) and Natural Science Foundation of Tianjin (Grant No.16JCYBJC15900). ...
doi:10.1109/iccvw.2017.370
dblp:conf/iccvw/WangWSL17
fatcat:rvangyx55nhi7glu7a757v5k6e
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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This paper presents an effective method for continuous gesture recognition. The method consists of two modules: segmentation and recognition. ...
Our method has been evaluated on ChaLearn LAP Large-scale Continuous Gesture Dataset and achieved the state-of-the-art performance. ...
Zhanjie Song was partly supported by National Natural Science Foundation of China (Grant No.61379014) and Natural Science Foundation of Tianjin (Grant No.16JCYBJC15900). ...
doi:10.1109/iccvw.2017.371
dblp:conf/iccvw/WangWSL17a
fatcat:edlsxxugb5dbpdu54bi7prgqyq
Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks
2022
Computers Materials & Continua
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This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. ...
Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. ...
In Section 3, the proposed technique which is based on 3D-CNN and LSTM for the recognition of dynamic hand gesture is presented. ...
doi:10.32604/cmc.2022.019586
fatcat:uxzj4v7ykjfyjhtby3cvho5qna
3D dynamic hand gestures recognition using the Leap Motion sensor and convolutional neural networks
[article]
2020
arXiv
pre-print
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The classification of the gestures is performed using a deep Convolutional Neural Network (CNN). ...
The method has been successfully applied to the existing reference dataset and preliminary tests have already been performed for the real-time recognition of dynamic gestures performed by users. ...
Recently, [37] proposes a system for the 3D dynamic hand gesture recognition by a deep learning architecture that uses a Convolutional Neural Network (CNN) applied on Discrete Fourier Transform on the ...
arXiv:2003.01450v3
fatcat:ji337dhemnd4pck7l7ks5zxoua
3D Hand Gesture Representation and Recognition through Deep Joint Distance Measurements
2020
International Journal of Advanced Computer Science and Applications
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The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods. The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%. ...
Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture. ...
Further, video-based 3D hand gesture recognition based on CNNs with datasets in [41] and [42] were also tested with our network. ...
doi:10.14569/ijacsa.2020.0110496
fatcat:6omi4iq4gjcudf2sqeh5deihxy
Application Of Convolution Neural Network To Recognize Hand Gestures
2021
Elementary Education Online
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In modern technical society, hand gestures-based computer applications are developed which motivates researchers to work on different techniques to recognize hand gestures. ...
Hand Gesture Recognition has many applications such in Human Computer Interaction applications. ...
A new deep learning-based technique was proposed which was giving commands to computer using six static and eight dynamic hand gestures. ...
doi:10.17051/ilkonline.2021.02.261
fatcat:swwc7icc3rgmde3gn4wbb2eawq
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework
[article]
2021
arXiv
pre-print
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The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with ...
Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. ...
In this work, we create an HCI system which is based on dynamic recognition of driver's micro hand gestures. ...
arXiv:2003.00951v2
fatcat:s54hoso6ybg5toncb3qegs5nva
Res3ATN - Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos
2019
2019 International Conference on 3D Vision (3DV)
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2020 pre-print arXiv:2001.01083v1 fatcat:i66ikifxzbayrblx6hlha2ymdq
Our 3D attention based residual network (Res3ATN) can be built and extended to very deep layers. ...
Using this network, an extensive analysis is performed on other 3D networks based on three publicly available datasets. ...
Video analysis and gesture recognition based on deep learning includes mainly three techniques based on how the temporal dimensions of the video data are treated [2] . ...
doi:10.1109/3dv.2019.00061
dblp:conf/3dim/DhingraK19
fatcat:qixha3frsnhktkoyczxb4wx4gm
Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network
2021
Computational Intelligence and Neuroscience
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To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. ...
The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared ...
, 29] . e dynamic gesture recognition networks based on deep learning are mainly divided into three types: two-stream networks, long short-term memory (LSTM) network, and three-dimensional convolutional ...
doi:10.1155/2021/4828102
pmid:34447430
pmcid:PMC8384521
fatcat:bb5m3n3znncwnfjsuhc33pzcxm
HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition
[article]
2021
arXiv
pre-print
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Considering the hierarchical structure of hand joints, we propose an efficient hierarchical self-attention network (HAN) for skeleton-based gesture recognition, which is based on pure self-attention without ...
Previous methods for skeleton-based gesture recognition mostly arrange the skeleton sequence into a pseudo picture or spatial-temporal graph and apply deep Convolutional Neural Network (CNN) or Graph Convolutional ...
Boulahia et al. [42] : They present a dynamic hand gesture recognition method based on 3D pattern assembled trajectories. • CNN-based methods. ...
arXiv:2106.13391v1
fatcat:vahfrfkxyvgk3gizv53nvojokm
Hand Gesture Recognition Based on Computer Vision: A Review of Techniques
2020
Journal of Imaging
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Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. ...
In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. ...
A new 3D hand gesture recognition approach based on a deep learning model using parallel convolutional neural networks (CNN) to process hand skeleton joints' positions was introduced in [59], the proposed ...
doi:10.3390/jimaging6080073
pmid:34460688
fatcat:zmid23k67vbozb54sfji4nlfiy
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