SOTAVerified

Gesture Recognition

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 526–550 of 572 papers

TitleStatusHype
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM—0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks—0
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition—0
Deep learning based Hand gesture recognition system and design of a Human-Machine Interface—0
DiG-Net: Enhancing Quality of Life through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics—0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing—0
Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment—0
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Dynamic Gesture Recognition—0
Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition—0
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks—0
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation—0
Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration—0
Ecological Gestures for HRI: the GEE Corpus—0
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition—0
Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks—0
Efficient Gesture Recognition on Spiking Convolutional Networks Through Sensor Fusion of Event-Based and Depth Data—0
Egocentric Gesture Recognition for Head-Mounted AR devices—0
Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules—0
Egocentric Hand Detection Via Dynamic Region Growing—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
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All datasetsDVS128 GestureCapgMyo DB-aCapgMyo DB-bCapgMyo DB-cChaLearn 2013Chalearn 2014ChaLearn 2016GesturePodMontalbanoMSRC-12Ninapro DB-1 12 gesturesNinapro DB-1 8 gestures

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TENNs-PLEIADESAccuracy (%)100—Unverified
2mMND (BPTT)Accuracy (%)98—Unverified
3EGRUAccuracy (%)97.8—Unverified
4AlexNet+LSTMAccuracy (%)97.73—Unverified
5S-TLLRAccuracy (%)97.72—Unverified
6Event-SSMAccuracy (%)97.7—Unverified
7STL-SNNAccuracy (%)97.22—Unverified
8OTTTAccuracy (%)96.88—Unverified
9STS-ResNetAccuracy (%)96.7—Unverified
10mMND (STDP)Accuracy (%)96.6—Unverified
#ModelMetricClaimedVerifiedStatus
12SRNNAccuracy97.1—Unverified
#ModelMetricClaimedVerifiedStatus
12SRNNAccuracy97.1—Unverified
#ModelMetricClaimedVerifiedStatus
12SRNNAccuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
13S Net TTMAccuracy92.08—Unverified
#ModelMetricClaimedVerifiedStatus
13D-CNN + LSTMAccuracy93.2—Unverified
#ModelMetricClaimedVerifiedStatus
13S Net TTMAccuracy39.95—Unverified
#ModelMetricClaimedVerifiedStatus
1GesturePodReal World Accuracy92—Unverified
#ModelMetricClaimedVerifiedStatus
1Temp Conv + LSTMError rate2.77—Unverified
#ModelMetricClaimedVerifiedStatus
13S Net TTMAccuracy99.01—Unverified
#ModelMetricClaimedVerifiedStatus
12SRNNAccuracy84.7—Unverified
#ModelMetricClaimedVerifiedStatus
12SRNNAccuracy90.7—Unverified
#ModelMetricClaimedVerifiedStatus
1PointLSTM14 gestures accuracy95.9—Unverified