A Novel Hand Gesture Recognition Based on High-Level Features

Li, Jing; Wang, Jianxin; Ju, Zhaojie

doi:10.1142/s0219843617500220

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…The cascade extraction technique was used with a novel recursive connected component algorithm. Another study by Li et al [19] presented a developed system to combine depth information and skeletal data, facing the challenge of complex background and illumination variation, rotation invariance, in which some constraints were set in hand segmentation. Another study by Ma et al [5] improved depth threshold segmentation by combining depth and colour information using the hierarchical scan method, and then hand segmentation was used based on the local neighbour method.…”

Section: Related Workmentioning

confidence: 99%

Elderly Care Based on Hand Gestures Using Kinect Sensor

2020

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Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.

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Section: Related Workmentioning

confidence: 99%

Elderly Care Based on Hand Gestures Using Kinect Sensor

2020

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“…In order to reduce the subsequent interference on the feature extraction of the gesture image, a gesture of elliptical model image segmentation is performed in the YCbCr color space expansion processing, and it can eliminate a small amount of holes in the gesture [34], the processing effect is shown in Figure 4 (a). Secondly, median filtering is performed, the contour of the gesture edge becomes very smooth [35], and the processing effect is shown in Figure 4(b).…”

Section: Post-processing Of Gesture Imagesmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

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With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

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“…al [16] proposed a gesture recognition system to segment the hand based on skin color and used K-means clustering and convex hull to identify hand contour and finally detect fingertips. Another study by Li et al [17] presents a developed system to combine depth information and skeletal data, facing the challenge of a complex background and illumination variation, rotation invariance, in which some constraints were set in hand segmentation. Marin et al [18] used two techniques together to detect finger regions such as leap motion and Kinect devices to extract different feature sets.…”

Section: Related Workmentioning

confidence: 99%

“…A threshold-based segmentation algorithm to z-axis was adopted to extract the hand mask. The resulting image was then smoothed by using a median filter [17]. The filtered image was combined with the cropped hand based on a joint tracking to improve the result of hand segmentation.…”

Section: 11the First Scenario: Hand Detection Using Depth Threshomentioning

confidence: 99%

Computer Vision for Elderly Care Based on Hand Gestures

Oudah¹,

Al-Naji²,

Chahl³

2020

Preprint

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Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

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A Novel Hand Gesture Recognition Based on High-Level Features

Cited by 14 publications

References 30 publications

Elderly Care Based on Hand Gestures Using Kinect Sensor

Elderly Care Based on Hand Gestures Using Kinect Sensor

Research on gesture recognition of smart data fusion features in the IoT

Computer Vision for Elderly Care Based on Hand Gestures

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