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 226–250 of 572 papers

TitleStatusHype
Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks—0
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation—0
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users—0
Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities—0
Gesture Recognition based on Long-Short Term Memory Cells using Smartphone IMUs—0
Gesture Recognition: Focus on the Hands—0
Gesture Recognition for FMCW Radar on the Edge—0
ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition—0
Gesture Recognition from Skeleton Data for Intuitive Human-Machine Interaction—0
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition—0
Gesture Recognition in Robotic Surgery: a Review—0
Gesture Recognition Portfolios for Personalization—0
Gesture Recognition using Reflected Visible and Infrared Light Wave Signals—0
Event-based Action Recognition Using Timestamp Image Encoding Network—0
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph—0
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild—0
Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles—0
Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned—0
Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardware—0
GLADAS: Gesture Learning for Advanced Driver Assistance Systems—0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
ChaLearn Looking at People: A Review of Events and Resources—0
Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)—0
CGAP2: Context and gap aware predictive pose framework for early detection of gestures—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—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