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Traffic Control Gesture Recognition for Autonomous Vehicles
[article]
2020
arXiv
pre-print
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In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. ...
Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. ...
In particular, we transform the LSTM to Attention-LSTM and make use of same architecture as before, however, empirically select 50 cells for the hidden layer and 50 attention units. c) Temporal Convolutional ...
arXiv:2007.16072v1
fatcat:p4tbiuy4yjarhfjsg4camldoo4
A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences
[article]
2022
arXiv
pre-print
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To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules ...
Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. ...
. • Stacked Bi-LSTM and Attention: To obtain the temporal information in the video stream, a stacked Bi-LSTM and Attention module is used. ...
arXiv:2207.07619v1
fatcat:oc6jgkkiqbelphxzn5dzffqxta
Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition
[chapter]
2019
Lecture Notes in Computer Science
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In this paper, we propose an end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels ...
Dynamic hand gesture recognition is a crucial yet challenging task in computer vision. ...
Even for the works on human action recognition, the attention modules in the existing literatures are mostly built on top of the Long Short-Term Memory (LSTM) recurrent networks. ...
doi:10.1007/978-3-030-11024-6_18
fatcat:issnlhukgffknhmd55pzwddypy
Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition
[chapter]
2018
Lecture Notes in Computer Science
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This deformable convolution better utilizes the contextual joints for action and gesture recognition and is more robust to noisy joints. ...
The representation of 3D pose plays a critical role for 3D action and gesture recognition. ...
The BeingTogether Centre is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative. ...
doi:10.1007/978-3-030-01234-2_9
fatcat:fhnrpe5qm5blzoygd6j3jnjonm
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
[article]
2021
arXiv
pre-print
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Here we propose the novel Temporal Convolutions-based Hand Gesture Recognition architecture (TC-HGR) to reduce this computational burden. ...
With this approach, we classified 17 hand gestures via surface Electromyogram (sEMG) signals by the adoption of attention mechanisms and temporal convolutions. ...
The proposed model showed strong capability in addressing several existing challenges of gesture recognition based on the temporal convolutions and attention mechanism. ...
arXiv:2110.08717v1
fatcat:rn5f3zgqwzgatconeehfbndemu
A Deep Learning Framework for Recognizing both Static and Dynamic Gestures
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.06321v1 fatcat:l2r5oxfrxfdgliaycprrhy6cyq
The Convolutional Neural Network in StaDNet is fine-tuned on a background-substituted hand gestures dataset. ...
This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. ...
In [34] , the authors studied redundancy and attention in ConvLSTM by deriving its several variants for gesture recognition. ...
arXiv:2006.06321v2
fatcat:bgruduvbzbedtibcy2gylsgk6e
Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network
2021
Computational Intelligence and Neuroscience
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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 ...
The temporal and spatial features are extracted by convolution of the video containing gesture. ...
In the dynamic gesture recognition, LSTM uses the common convolutional network to extract the features, serializes the spatial features extracted by the previous network through LSTM, and then classifies ...
doi:10.1155/2021/4828102
pmid:34447430
pmcid:PMC8384521
fatcat:bb5m3n3znncwnfjsuhc33pzcxm
Ultrasound based gesture recognition
2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Thereafter, we use a combined Convolutional (CNN) and Long Short-Term Memory (LSTM) network to recognize gestures from the ultrasound images. ...
We report gesture recognition accuracies in the range 64.5-96.9%, based on the number of gestures to be recognized, and show that ultrasound sensors have the potential to become low power, low computation ...
Fig. 3 : 3 CNN-LSTM architecture for gesture recognition
Fig. 4 : 4 Optical and ultrasonic images of different gestures
, Bloom, Poke, Attention, Random • CAT 4a: Tap, Bloom, Poke, Attention • CAT ...
doi:10.1109/icassp.2017.7952187
dblp:conf/icassp/DasTM17
fatcat:rpn65gmmxjc7ddewvu75d3746m
DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition
2020
IEEE Access
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Hand gesture recognition has attracted the attention of many researchers due to its wide applications in robotics, games, virtual reality, sign language and human-computer interaction. ...
) for sequence recognition. ...
ACKNOWLEDGMENT The authors extend their appreciation to the Deanship of Scientific Research at Majmaah University for funding this work. ...
doi:10.1109/access.2020.2990699
fatcat:rnov5ewiprdcrh2jf5h6em626q
GestureVLAD: Combining Unsupervised Features Representation and Spatio-Temporal Aggregation for Doppler-Radar Gesture Recognition
2019
IEEE Access
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In this paper we propose a novel framework to process Doppler-radar signals for hand gesture recognition. ...
In this scope, current recognition methods still rely in deep CNN-LSTM and 3D CNN-LSTM structures that require sufficient labelled data to optimize millions of parameters and significant amount of computational ...
[29] made use of 3D convolution along with LSTM for Doppler-based hand gesture recognition. ...
doi:10.1109/access.2019.2942305
fatcat:x37fqsbpmzdujdbrhmdtx5nrta
Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network
2021
Electronics
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Traffic police gesture recognition is important in automatic driving. ...
To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed ...
Acknowledgments: We gratefully acknowledge the assistance of Neurocomputing, 390, He J, Zhang C, He X, Dong R, Visual Recognition of traffic police gestures with convolutional pose machine and handcrafted ...
doi:10.3390/electronics10050551
fatcat:q5zbhbzjsnfotpjuh6dyhmuw2i
Review of dynamic gesture recognition
2021
Virtual Reality & Intelligent Hardware
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To help researchers better understanding the development status of gesture recognition in video, this article provides a detailed survey of the latest developments in gesture recognition technology for ...
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 ...
This approach utilizes 3D convolutional LSTM [53] to recognize dynamic gestures in video, and ueses convolutional networks to recognize gestures in dynamic image sequences constructed by rank pooling ...
doi:10.1016/j.vrih.2021.05.001
fatcat:jpddnlf2xbfufnyuf3s6fbxgty
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences
2017
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
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In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. ...
The interest in action and gesture recognition has grown considerably in the last years. ...
Today, LSTMs are an important part of deep models for image sequence modeling for human action/gesture recognition [98, 92] . ...
doi:10.1109/fg.2017.150
dblp:conf/fgr/Asadi-Aghbolaghi17
fatcat:wzkf5sfc5ncsfjicmkfuw4owxq
Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition
2019
EURASIP Journal on Image and Video Processing
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Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition. ...
Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. ...
Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions. 2 ...
doi:10.1186/s13640-019-0476-x
fatcat:avjrxn7prbcoxetfbt6njmgqhu
Multi-information Spatial–temporal LSTM Fusion Continuous Sign Language Neural Machine Translation
2020
IEEE Access
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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Nos. 60972095, 61271362, 61671362,62071366) and the Natural Science Basic Research Plan of Shaanxi Province in ...
[30] used a deep belief network to extract high-level skeletal joint features for gesture recognition. ...
[27] proposed an end-to-end neural model based on time convolution and bidirectional recursion for sign language recognition. ...
doi:10.1109/access.2020.3039539
fatcat:dcxkodn3vbaavdiixcko5zooyy
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