Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Jian, Chengfeng; Li, Junjie

doi:10.1049/iet-ipr.2019.1068

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…We introduce tip-point detection for hand tracking. Various methods have been purposed in literature for hand tracking [23], determining tip points is also a common approach for fingers (e.g., [24]). In our situation, the camera is positioned above the sink and subject is always facing upside down in images, we can assume that the tip of hands will always be in the lowest white pixel at y-coordinate of the binary image which was acquired after pre-processing.…”

Section: Hand Detectionmentioning

confidence: 99%

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Ozakar

Gedikli

2023

Electronics

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Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

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Section: Hand Detectionmentioning

confidence: 99%

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Ozakar

Gedikli

2023

Electronics

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“…From spatial features in static gestures, temporal features are added to create dynamic gestures. According to [9], dynamic gesture recognition is widely used because it conveys more information.…”

Section: Introductionmentioning

confidence: 99%

“…References [10][11][12][13][14] use deep learning approaches with spatial-temporal features in their work to achieve dynamic gesture recognition. Three deep learning models that have been built for HGR are 3DCNN, CNN-RNN, and CNN-Transformer.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Based on Deep Learning for Muslim Elderly Care

Marzuki¹,

Hashim²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Gesture recognition for elderly care is an approach to classify gestures performed by the elderly to convey specific messages. Nursing homes or caretakers are often hired to take care of senior citizens and are responsible to keep them safe. Hence, this study will be useful to assist caretakers in providing needs requested by the elderly when they are absent. A collection of dynamic hand gesture recognition (HGR) datasets consisting of ten gesture classes with 630 videos is used to build the models. The ten gestures will represent requests for help to perform daily activities namely eating, toileting and dressing. Specifically for this research, we have infused Islamic hand gestures such as gestures to perform prayers and read the Quran. In this paper, we adopted the action recognition pre-trained model into the HGR by using CNN RNN and Transformer with CNN models. The result of this study shows that Transformer with CNN model has higher accuracy in recognizing hand gestures compared to CNN-RNN model.

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“…Hand gesture recognition can be divided into static hand gesture recognition and dynamic hand gesture recognition. [15][16][17] Static hand gesture recognition obtains the meaning represented by each category of hand gesture through the processing of the hand gesture images according to the combining state of different fingers stretching out, while dynamic hand gesture recognition identifies the meaning expressed by the hand gesture through the processing of the hand gesture videos according to the trajectory, velocity and angle of the hand motion. Although the research on gesture recognition has lasted for decades, it is still an open problem now.…”

Section: Introductionmentioning

confidence: 99%

Static hand gesture recognition based on hierarchical decision and classification of finger features

Zhang

2022

Science Progress

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Considering that the distinctions among static hand gestures are the difference between fingers sticking out, a method of grouping and classifying hand gestures step by step by using the information of the quantity, direction, position and shape of the outstretched fingers was proposed this paper. Firstly, the gesture region was segmented by using the skin color information of the hand, and the gesture direction was normalized by using the direction information of the gesture contour lines. Secondly, the finger was segmented one by one by using convex decomposition in the hand gesture image based on the convex characteristic of the gesture shape. Thirdly, the features of quantity, direction, position and shape of the segmented fingers were extracted. Lastly, a hierarchical decision classifier embedded with deep sparse autoencoders was constructed. The quantity of fingers was used to divide the gesture images into groups first, then the direction, position and shape features of the fingers were used to subdivide and recognize gestures within each group. The experimental results show that the proposed method is robust as lighting, direction and scale changes and significantly superior to the traditional method both in the recognition rate and the recognition stability.

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Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Cited by 4 publications

References 28 publications

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Dynamic Hand Gesture Recognition Based on Deep Learning for Muslim Elderly Care

Static hand gesture recognition based on hierarchical decision and classification of finger features

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