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computer-vision-2

Hand Gesture Recognition

3260 papers • 126 benchmarks • 313 datasets

Hand gesture recognition (HGR) is a subarea of Computer Vision where the focus is on classifying a video or image containing a dynamic or static, respectively, hand gesture. In the static case, gestures are also generally called poses. HGR can also be performed with point cloud or joint hand data.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in hand-gesture-recognition-8

Trend

Dataset

Best Model

Actions

DHG-14

DHG-14

DHG-28

DHG-28

NVGesture

NVGesture

Libraries

i

Use these libraries to find hand-gesture-recognition-8 models and implementations

Datasets

EgoGesture

SHREC

NVGesture

Jester (Gesture Recognition)

Jester (Gesture Recognition)

IPN Hand

VIVA

Subtasks

No subtasks available.

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.

225

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

SHREC 2017

SHREC 2017

EgoGesture

EgoGesture

SHREC 2017 track on 3D Hand Gesture Recognition

SHREC 2017 track on 3D Hand Gesture Recognition

ChaLearn val

ChaLearn val

VIVA Hand Gestures Dataset

VIVA Hand Gestures Dataset

LSA16

LSA16

Jester test

Jester test

Cambridge

Cambridge

RWTH-PHOENIX Handshapes dev set

RWTH-PHOENIX Handshapes dev set

MGB

MGB

BUAA

BUAA

SmartWatch

SmartWatch

Jester val

Jester val

ChaLean test

ChaLean test

Northwestern University

Northwestern University

RWTH-PHOENIX Handshapes dev set

RWTH-PHOENIX Handshapes dev set

TIM-Tremor

OREBA

LSA16

0

Make Skeleton-based Action Recognition Model Smaller, Faster and Better

S. Sakti, Satoshi Nakamura, Fan Yang, Yang Wu•Mon Jul 22 2019

A Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition, which can reach a super fast speed, and achieves state-of-the-art performance on experiment datasets: SHREC and JHMDB.

172 0

Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Hongdong Li, Dongxu Li, Xin Yu, Cristian Rodriguez-Opazo•Wed Oct 23 2019

This paper introduces a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers, and proposes a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose- based method.

553 0

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

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.

6 0

First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

Guillermo Garcia-Hernando, Shanxin Yuan, Seungryul Baek, Tae-Kyun Kim•Fri Apr 07 2017

This work collects RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations, and sees clear benefits of using hand pose as a cue for action recognition compared to other data modalities.

529 0

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

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

Adding a benchmark result helps the community track progress.

Hand Gesture Recognition | State-of-the-Art