Hand Gesture Recognition
41 papers with code • 18 benchmarks • 14 datasets
Hand gesture recognition (HGR) is a subarea of Computer Vision where the focus is on classifying a video or image containing a dynamic or static, respectively, hand gesture. In the static case, gestures are also generally called poses. HGR can also be performed with point cloud or joint hand data.
Benchmarks
These leaderboards are used to track progress in Hand Gesture Recognition
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Datasets
EgoGesture
SHREC
NVGesture
Jester (Gesture Recognition)
IPN Hand
VIVA
RWTH-PHOENIX Handshapes dev set
OREBA
LSA16
Latest papers
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
Moreover, we show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6. 19x, while the fused-weight attention can reduce the runtime by 1. 53x, and number of parameters by 25%.
CASTER: A Computer-Vision-Assisted Wireless Channel Simulator for Gesture Recognition
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library
Hand gesture recognition systems provide a natural way for humans to interact with computer systems.
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
liujf69/TD-GCN-Gesture
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IEEE Transactions on Multimedia 2023
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation
Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR.
A comparison of small sample methods for Handshape Recognition
midusi/cacic2019-handshapes
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Journal of Computer Science and Technology (JCST) 2023
We compare a series of models specially tailored for small datasets to improve their performance on handshape recognition tasks.
Data-Free Class-Incremental Hand Gesture Recognition
humansensinglab/dfcil-hgr
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ICCV 2023
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models
Savings were defined as the number of additional observations required when a Deep Learning model is trained on new hand gestures instead of a Few Shot Learning model.
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains
MansoorehMontazerin/Vision-Transformer_EMG
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Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals.
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
mims-harvard/tfc-pretraining
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Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
