Unified learning approach for egocentric hand gesture recognition and fingertip detection
Reads0
Chats0
TLDR
In this article, a unified approach of egocentric hand gesture recognition and fingertip detection is introduced, which uses a single convolutional neural network to predict the probabilities of finger class and positions of fingertips in one forward propagation.About:
This article is published in Pattern Recognition.The article was published on 2022-01-01 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Computer science & Gesture.read more
Citations
More filters
Journal ArticleDOI
Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique
TL;DR: A system has been developed that will use the visual hand dataset based on an Arabic Sign Language and interpret this visual data in textual information and the EfficientNetB4 model has been considered the best fit for the case.
Journal ArticleDOI
Hand gesture recognition framework using a lie group based spatio-temporal recurrent network with multiple hand-worn motion sensors
Shu Wang,A. Wang,Mengyuan Ran,Li Liu,Yuxin Peng,Ming Li,Guoxin Su,Adi Alhudhaif,Fayadh Alenezi,Norah Alnaim +9 more
TL;DR: Li et al. as discussed by the authors proposed a spatio-temporal framework, named STGauntlet, that explicitly characterizes the hand motion context of spatiotemporal relations among multiple bones and detects hand gestures in real-time.
Journal ArticleDOI
Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet
Abeer Ali Alnuaim,Mohammed Zakariah,Wesam Atef Hatamleh,Hussam Tarazi,Vikas Tripathi,Enoch Tetteh Amoatey +5 more
TL;DR: A model is trained, which will be able to classify the Arabic sign language, which consists of 32 Arabic alphabet sign classes, which is released in 2019 and is called as ArSL2018.
Journal ArticleDOI
3D hand pose and shape estimation from RGB images for keypoint-based hand gesture recognition
TL;DR: In this article , a keypoint-based end-to-end framework for 3D hand and pose estimation was proposed, which uses a multi-task semantic feature extractor to generate 2D heatmaps and hand silhouettes from RGB images, a viewpoint encoder to predict the hand and camera view parameters, a stable hand estimator to produce the 3D shape, and a loss function to guide all of the components jointly during the learning phase.
Journal ArticleDOI
Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model
TL;DR: In this paper , the authors provide a concise analysis of CNN-based object recognition algorithms, more specifically, the Yolov7 and YOLov7x models with 100 and 200 epochs.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
YOLO9000: Better, Faster, Stronger
Joseph Redmon,Ali Farhadi +1 more
TL;DR: YOLO9000 as discussed by the authors is a state-of-the-art real-time object detection system that can detect over 9000 object categories in real time using a novel multi-scale training method, offering an easy tradeoff between speed and accuracy.
Posted Content
MobileNetV2: Inverted Residuals and Linear Bottlenecks
TL;DR: A new mobile architecture, MobileNetV2, is described that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes and allows decoupling of the input/output domains from the expressiveness of the transformation.
Journal ArticleDOI
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor
TL;DR: A novel distance metric, Finger-Earth Mover's Distance (FEMD), is proposed, which only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences.