A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Journal of Imaging, Год журнала: 2022, Номер 8(6), С. 153 - 153

Опубликована: Май 26, 2022

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven recognition system in real time has remained a challenge. This paper aims uncover the limitations faced image acquisition through use of cameras, segmentation and tracking, feature extraction, classification stages various camera orientations. looked at research systems from 2012 2022. Its goal is find areas that are getting better those need more work. We used specific keywords 108 articles well-known online databases. In this article, we put together collection most notable works related suggest different categories for recognition-related with subcategories create valuable resource domain. summarize analyze methodologies tabular form. After comparing similar types field, drawn conclusions based our findings. Our also how well recognized gestures terms accuracy. There wide variation identification accuracy, 68% 97%, average being 86.6 percent. The considered comprise multiple text interpretations complex non-rigid characteristics. comparison current research, unique it discusses all techniques.

Язык: Английский

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Hypertuned Deep Convolutional Neural Network for Sign Language Recognition

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 10

Опубликована: Апрель 30, 2022

Sign language plays a pivotal role in the lives of impaired people having speaking and hearing disabilities. They can convey messages using hand gesture movements. American Language (ASL) recognition is challenging due to increasing intra-class similarity high complexity. This paper used deep convolutional neural network for ASL alphabet overcome challenges. presents an approach network. The performance DeepCNN model improves with amount given data; this purpose, we applied data augmentation technique expand size training from existing artificially. According experiments, proposed provides consistent results dataset. Experiments prove that gives better accuracy gain 19.84%, 8.37%, 16.31%, 17.17%, 5.86%, 3.26% as compared various state-of-the-art approaches.

Язык: Английский

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An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process

ISA Transactions, Год журнала: 2022, Номер 136, С. 139 - 151

Опубликована: Ноя. 2, 2022

Язык: Английский

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Harris Hawk Optimization: A Survey onVariants and Applications

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 20

Опубликована: Июнь 27, 2022

In this review, we intend to present a complete literature survey on the conception and variants of recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set applications in well-established works. For purpose, first overview HHO, including its logic equations mathematical model. Next, focus reviewing different HHO from available literature. To provide readers deep vision foster application review state-of-the-art improvements focusing mainly fuzzy new intuitionistic algorithm. We also enhancing machine learning operations tackling engineering problems. This can cover aspects future basis for research development swarm intelligence paths use real-world

Язык: Английский

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An improved human activity recognition technique based on convolutional neural network

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Дек. 19, 2023

A convolutional neural network (CNN) is an important and widely utilized part of the artificial (ANN) for computer vision, mostly used in pattern recognition system. The most applications CNN are medical image analysis, classification, object from videos, recommender systems, financial time series natural language processing, human-computer interfaces. However, after technological advancement power computing ability emergence huge quantities labeled data provided through enhanced algorithms, nowadays, almost every area study. One main uses wearable technology within surveillance human activity (HAR), which must require constant tracking everyday activities. This paper provides a comprehensive study application CNNs classification HAR tasks. We describe their enhancement, antecedents up to current state-of-the-art systems deep learning (DL). have working principle tasks, CNN-based model presented perform proposed technique interprets sensor sequences inputs by using multi-layered that gathers temporal spatial related publicly available WISDM dataset has been this two-dimensional approach make different recent version Python software rate accuracy experiment 97.20%, better than previously estimated technique. findings imply DL methods might greatly increase range where can be successfully. also described future research trends field article.

Язык: Английский

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CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Sensors, Год журнала: 2022, Номер 22(10), С. 3782 - 3782

Опубликована: Май 16, 2022

Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and spatial resolution resulting in a severe lack of texture details for small infrared targets, as well distribution extremely multiscale ships. In this paper, we propose CAA-YOLO alleviate problems. study, highlight preserve features apply high-resolution feature layer (P2) better use shallow location information. order suppress noise P2 further enhance extraction capability, introduce TA module into backbone. Moreover, design new fusion method capture long-range contextual information targets combined attention mechanism ability while suppressing interference caused by layers. We conduct detailed study algorithm based on marine dataset verify effectiveness our algorithm, which AP AR increase 5.63% 9.01%, respectively, mAP increases 3.4% compared that YOLOv5.

Язык: Английский

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Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier

Applied Sciences, Год журнала: 2022, Номер 12(15), С. 7643 - 7643

Опубликована: Июль 29, 2022

Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as meaningful physical movement fingers, hands, arms, or other parts body with purpose to convey information environment interaction. For instance, hand (HGR) used recognize sign language which is primary means communication by deaf mute. Vision-based HGR critical in its application; however, there are challenges that will need overcome such variations background, illuminations, orientation size similarities among gestures. The traditional machine learning approach widely vision-based recent years but complexity processing major challenge—especially on handcrafted feature extraction. effectiveness extraction technique was not proven across various datasets comparison deep techniques. Therefore, hybrid network architecture dubbed Lightweight VGG16 Random Forest (Lightweight VGG16-RF) proposed model adopts techniques via convolutional neural (CNN) using method perform classification. Experiments were carried out publicly available American Sign Language (ASL), ASL Digits NUS Hand Posture dataset. experimental results demonstrate model, combination lightweight random forest, outperforms methods.

Язык: Английский

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Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

Journal of Bionic Engineering, Год журнала: 2022, Номер 20(3), С. 1153 - 1174

Опубликована: Ноя. 30, 2022

Язык: Английский

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An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models

Computers & Electrical Engineering, Год журнала: 2023, Номер 111, С. 108923 - 108923

Опубликована: Авг. 22, 2023

Язык: Английский

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Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNN

Frontiers in Bioengineering and Biotechnology, Год журнала: 2024, Номер 12

Опубликована: Июль 31, 2024

Hand gestures are an effective communication tool that may convey a wealth of information in variety sectors, including medical and education. E-learning has grown significantly the last several years is now essential resource for many businesses. Still, there not been much research conducted on use hand e-learning. Similar to this, frequently used by professionals help with diagnosis treatment.

Язык: Английский

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