Gesture Recognition
118 papers with code • 13 benchmarks • 14 datasets
Gesture Recognition is an active field of research with applications such as automatic recognition of sign language, interaction of humans and robots or for new ways of controlling video games.
Source: Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping
Benchmarks
These leaderboards are used to track progress in Gesture Recognition
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Libraries
Use these libraries to find Gesture Recognition models and implementationsDatasets
Aff-Wild
DVS128 Gesture
SHREC
Jester (Gesture Recognition)
TCG
Latest papers
Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks
Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision.
Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification
In this paper, we briefly introduce the solution of our team HFUT-VUT for the Micros-gesture Classification in the MiGA challenge at IJCAI 2023.
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.
SGED: A Benchmark dataset for Performance Evaluation of Spiking Gesture Emotion Recognition
Moreover, we propose a pseudo dual-flow network based on this dataset, and verify the application potential of this dataset in the affective computing community.
Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots
miguelsimao/uc2017_classification
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The classification models show an accuracy of 95. 6% for a library of 24 SGs with a random forest and 99. 3% for 10 DGs using artificial neural networks.
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.
Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference
It improves TinyML memory optimization significantly by reducing memory of models where this was not possible before and additionally providing alternative design points for models that show high run time overhead with existing methods.
Fine-tuning of sign language recognition models: a technical report
ds-hub-sochi/sl-techreport
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We also investigated how the additional training of the model in another sign language affects the quality of recognition.
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition.
The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
intelligent-microsystems-lab/snnquantprune
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Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the biological brain.
