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 with no code
Hand Shape and Gesture Recognition using Multiscale Template Matching, Background Subtraction and Binary Image Analysis
This paper presents a hand shape classification approach employing multiscale template matching.
Spiking Neural Network Enhanced Hand Gesture Recognition Using Low-Cost Single-photon Avalanche Diode Array
We present a compact spiking convolutional neural network (SCNN) and spiking multilayer perceptron (SMLP) to recognize ten different gestures in dark and bright light environments, using a $9. 6 single-photon avalanche diode (SPAD) array.
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications.
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method.
Efficient Gesture Recognition on Spiking Convolutional Networks Through Sensor Fusion of Event-Based and Depth Data
As intelligent systems become increasingly important in our daily lives, new ways of interaction are needed.
Towards Open-World Gesture Recognition
We propose leveraging continual learning to make machine learning models adaptive to new tasks without degrading performance on previously learned tasks.
Simultaneous Gesture Classification and Localization with an Automatic Gesture Annotation Model
Training a real-time gesture recognition model heavily relies on annotated data.
Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation
This work proposes a novel approach for hand gesture recognition using an inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking Neural Network (SNN) followed by Sparse Segmentation and feature-based gesture classification via Robust Principal Component Analysis (R-PCA).
A multimodal gesture recognition dataset for desktop human-computer interaction
Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity.
Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets
sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions.
