Hand-Gesture Recognition
31 papers with code • 2 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Hand-Gesture Recognition
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Datasets
IPN Hand
VIVA
MLGESTURE DATASET
Human Palm and Gloves Dataset | Human Body Parts Dataset
Latest papers
ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals
MansoorehMontazerin/Vision-Transformer_EMG
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The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning.
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
multimodallearning/hand-gesture-posture-position
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3DV 2022
To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams.
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
willogy-team/hand-gesture-recognition-smc2021
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This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH".
LIGAR: Lightweight General-purpose Action Recognition
openvinotoolkit/training_extensions
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Furthermore, the induced label noise problem is formulated and Adaptive Clip Selection (ACS) framework is proposed to deal with it.
A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System
ID56/Multimodal-Fusion-CRNN
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In this paper, we revisit this approach to hand gesture recognition and suggest several improvements.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
Force myography benchmark data for hand gesture recognition and transfer learning
We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research.
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 100 mW is achieved.
Low-latency hand gesture recognition with a low resolution thermal imager
EAVISE/hand-gesture-recognition
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Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars.
IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR.
