computer-vision-4

Hand-Gesture Recognition

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in hand-gesture-recognition-17

Trend

Dataset

Best Model

Actions

VIVA Hand Gestures Dataset

InAirGestures

Libraries

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Use these libraries to find hand-gesture-recognition-17 models and implementations

Datasets

Human Palm and Gloves Dataset | Human Body Parts Dataset

Subtasks

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Most implemented papers

Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks

G. Rigoll, Okan Köpüklü, Ahmet Gunduz, Neslihan Kose•Mon Jan 28 2019

A hierarchical structure enabling offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach is proposed by proposing a lightweight CNN architecture to detect gestures and a classifier which is a deep CNN to classify the detected gestures.

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HGR-Net: a fusion network for hand gesture segmentation and recognition

M. Mirmehdi, Amirhossein Dadashzadeh, Alireza Tavakoli Targhi, Maryam Tahmasbi•Wed Jun 13 2018

This work proposes a two-stage convolutional neural network architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.

79 0

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Human Computer Interaction Using Marker Based Hand Gesture Recognition

Sayem Mohammad Siam, Jahidul Adnan Sakel, M. H. Kabir•Wed Jun 22 2016

This paper proposes a new method of interaction with computing devices having a consumer grade camera that uses two colored markers worn on tips of the fingers to generate desired hand gestures, and for marker detection and tracking the authors used template matching with kalman filter.

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Deep Fisher discriminant learning for mobile hand gesture recognition

Chen Chen, Jungong Han, Ce Li, Chunyu Xie, Baochang Zhang•Tue Jul 11 2017

Two new deep models termed as F-BLSTM and F-BGRU are proposed, which can effectively classify the gesture based on analyzing the acceleration and angular velocity data of the human gestures.

67 0

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Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition

Y. Satoh, Hirokatsu Kataoka, Kensho Hara•Thu Aug 24 2017

The 3D ResNets trained on the Kinetics did not suffer from overfitting despite the large number of parameters of the model, and achieved better performance than relatively shallow networks, such as C3D.

668 0

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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition

G. Rigoll, Okan Köpüklü, Neslihan Köse•Wed Apr 18 2018

This paper proposes a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action.

121 0

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Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training

Vishal M. Patel, Hamid Reza Vaezi Joze, Mahdi Abavisani•Thu Dec 13 2018

This work presents an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition, and introduces a "spatiotemporal semantic alignment" loss (SSA) to align the content of the features from different networks.

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Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion

N. Sebe, Hao Tang, Hong Liu, Wei Xiao•Mon Jan 14 2019

This work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition.

109 0

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Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

Dimitris N. Metaxas, Yuxiao Chen, Long Zhao, Xi Peng, Jianbo Yuan•Fri Jul 19 2019

A Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition to first construct a fully-connected graph from a hand skeleton, where the node features and edges are automatically learned via a self-attention mechanism that performs in both spatial and temporal domains.

109 0

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