Hand pose estimation with multi-scale network

Hu, Zhongxu; Hu, Youmin; Wu, Bo; Liu, Jie; Han, Dongmin

doi:10.1007/s10489-017-1092-z

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…In [34], the researcher proposed using a glove with instruments to create a system for recognizing Arabic SL utilizing hidden Markov models and spatiotemporal features for the continuous recognition of Arabic SL. The authors of [35] advocated using a multiscale network for hand pose estimation. Similarly, ref.…”

Section: Related Workmentioning

confidence: 99%

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

Islam,

Aloraini,

Aladhadh

et al. 2023

Sensors

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Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model’s effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model’s efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications.

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Section: Related Workmentioning

confidence: 99%

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

Islam,

Aloraini,

Aladhadh

et al. 2023

Sensors

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“…A typical area of interest in HMI is command recognition, which involves two widely studied tasks: activity and speech recognition. Hand gesture recognition is a commonly used activity recognition technique and has been implemented in several commercial vehicles [279][280][281]. In-vehicle hand gesture recognition enables the user to interact with the vehicle through static or dynamic gestures.…”

Section: B Ddt In Personalized Human-machine Interfacementioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

Self Cite

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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“…ey presented constant detection of Arabic sign language employing hidden Markov models and spatiotemporal characteristics. Hand pose estimation with a multiscale network was proposed by [38]. Similarly, [39] studied translation from Arabic sign language to text, which may be utilized on portable devices.…”

Section: Related Workmentioning

confidence: 99%

Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique

Zakariah

Alotaibi

Koundal

et al. 2022

Computational Intelligence and Neuroscience

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Sign language is essential for deaf and mute people to communicate with normal people and themselves. As ordinary people tend to ignore the importance of sign language, which is the mere source of communication for the deaf and the mute communities. These people are facing significant downfalls in their lives because of these disabilities or impairments leading to unemployment, severe depression, and several other symptoms. One of the services they are using for communication is the sign language interpreters. But hiring these interpreters is very costly, and therefore, a cheap solution is required for resolving this issue. Therefore, 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. The dataset used consists of 54049 images of Arabic sign language alphabets consisting of 1500\ images per class, and each class represents a different meaning by its hand gesture or sign. Various preprocessing and data augmentation techniques have been applied to the images. The experiments have been performed using various pretrained models on the given dataset. Most of them performed pretty normally and in the final stage, the EfficientNetB4 model has been considered the best fit for the case. Considering the complexity of the dataset, models other than EfficientNetB4 do not perform well due to their lightweight architecture. EfficientNetB4 is a heavy-weight architecture that possesses more complexities comparatively. The best model is exposed with a training accuracy of 98 percent and a testing accuracy of 95 percent.

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Hand pose estimation with multi-scale network

Cited by 15 publications

References 34 publications

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique

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