Convolutional neural network with a DAG architecture for control of a robotic arm by means of hand gestures

Pinzón-Arenas, Javier Orlando; Moreno, Róbinson Jiménez; Beleño, Ruben Darío Hernández

doi:10.12988/ces.2018.8241

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…In view of this, researchers have begun utilising deep learning approaches such as CNNs [ 1 , 2 , 3 , 4 , 6 , 17 , 27 , 28 , 29 , 30 , 31 , 32 ] and ANNs [ 33 ] over conventional hand-crafted methods. Deep learning approaches have the ability to automatically discover complex and important features through their hidden layers, saving time and reducing bias during feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…L2 regularization was used to penalize large magnitude weights and reduce the model’s complexity. Moreover, in [ 29 ], a Directed Acyclic Graph (DAG)-CNN network architecture for recognising hand gestures is proposed. The Inception architecture is utilised to increase the network’s depth and learn more features for each gesture category, compensating for variations in lighting and noise.…”

Section: Related Workmentioning

confidence: 99%

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HGR-ViT: Hand Gesture Recognition with Vision Transformer

Tan

Lim

Chang

et al. 2023

Sensors

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Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

HGR-ViT: Hand Gesture Recognition with Vision Transformer

Tan

Lim

Chang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In this section, we employ three benchmark datasets that are commonly used by researchers in the field of static hand gesture recognition [3,6,26,[41][42][43][44] to assess the effectiveness of the proposed SDViT method. These datasets encompass the ASL, ASL with digits, and NUS hand gesture datasets.…”

Section: Experiments and Analysismentioning

confidence: 99%

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Tan,

Lim,

Lee

et al. 2023

Applied Sciences

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Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HGR faces challenges such as the high similarity of hand gestures, real-time performance, and model generalization. To address these challenges, this paper proposes the stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. An initially pretrained vision transformer (ViT) featuring a self-attention mechanism is introduced to effectively capture intricate connections among image patches, thereby enhancing its capability to handle the challenge of high similarity between hand gestures. Subsequently, knowledge distillation is proposed to compress the ViT model and improve model generalization. Multiple distilled ViTs are then stacked to achieve higher predictive performance and reduce overfitting. The proposed SDViT model achieves a promising performance on three benchmark datasets for hand gesture recognition: the American Sign Language (ASL) dataset, the ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. The accuracies achieved on these datasets are 100.00%, 99.60%, and 100.00%, respectively.

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“…is model consists of two convolutional layers, two fully connected layers, parallel classification layers, and regression layers. e output result is five-dimensional data, including whether the frame has an identity of the target and the 4 position parameters of the frame [11]. In order to avoid the situation that the candidate frame is too close to the object, the network model can predict the boundary of the object that the initial candidate frame does not fully contain, and the area expanded by a constant coefficient c is used as the new search area R [12] through the candidate frame.…”

Section: Structural Design Of Target Positioning Optimizationmentioning

confidence: 99%

Sports Biology Seminar of Three-dimensional Movement Characteristics of Yoga Standing Based on Image Recognition

Zheng

Sun

et al. 2022

Scientific Programming

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With the exponential improvement of the integration capabilities of electronic hardware devices, digital images have become an indispensable information carrier, thus promoting the development of image recognition, detection, or tracking technologies. At present, there is a blank in the field of image recognition measured by the action standard of yoga standing three-dimensional movement, which will bring huge room for researchers to play. Based on the traditional image recognition technology, there are cases where missed detection or false detection cannot be correctly identified and made a correct judgment. In this paper, the ability to capture the action is improved by optimizing the network for the positioning of the stereoscopic action target of the yoga station. The linear discriminant analysis (LDA) after adding optimization is used to reduce the dimensionality of the captured image data, which is beneficial to improving image recognition rate and reducing image loss rate. Through the analysis of the three-dimensional movement of the yoga station, the model based on the improved algorithm is compared with the traditional model, and the final image recognition accuracy is improved compared with the network model before the improvement. The image recognition error rate steadily tends to 5%, and the loss rate is also below 2%. Through the optimized convolutional neural network, this paper can accurately capture the image position, and the recognition rate has also been greatly improved, which can provide a reference for future research in other fields.

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Convolutional neural network with a DAG architecture for control of a robotic arm by means of hand gestures

Cited by 17 publications

References 11 publications

HGR-ViT: Hand Gesture Recognition with Vision Transformer

HGR-ViT: Hand Gesture Recognition with Vision Transformer

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Sports Biology Seminar of Three-dimensional Movement Characteristics of Yoga Standing Based on Image Recognition

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