Finger application using K-Curvature method and Kinect sensor in real-time

Bakar, M. Zabri Abu; Samad, Rosdiyana; Pebrianti, Dwi; Mustafa, Mahfuzah; Abdullah, Nor Rul Hasma

doi:10.1109/istmet.2015.7359032

Cited by 13 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The K-curvature is a famous algorithm used to detect the certain shape of an object [33] as well as for fingertip detection. One fingertip-based hand gesture application was developed using the K-curvature method and Microsoft's Kinect sensor [34]. However, this method showed some limitations regarding hand gestures, specifically with the accuracy of the gesture detection and the fact that the user only uses one hand to perform in front of the camera without using their body.…”

Section: Hand Gesture Recognition Using Rgb-d Sensormentioning

confidence: 99%

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Tran

Yang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.

show abstract

Section: Hand Gesture Recognition Using Rgb-d Sensormentioning

confidence: 99%

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Tran

Yang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In recent years, traditional camera-based approaches that detect the area of the hand and recognize hand gestures have been developed [1,2,6,13,17,19,21,24,25,31,34]. These approaches had obvious detection difficulty when the light levels were changed or a complex background was used and required a fixed distance from the camera to the users.…”

Section: Related Workmentioning

confidence: 99%

“…The virtual monitor concept was first introduced in [2,15]. It is defined as a virtual space between a Kinect device and a user where a mouse cursor can be controlled by the hands.…”

Section: Virtual Screen Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Real-time virtual mouse system using RGB-D images and fingertip detection

Tran

Yang

et al. 2020

Multimed Tools Appl

View full text Add to dashboard Cite

A real-time fingertip-gesture-based interface is still challenging for human–computer interactions, due to sensor noise, changing light levels, and the complexity of tracking a fingertip across a variety of subjects. Using fingertip tracking as a virtual mouse is a popular method of interacting with computers without a mouse device. In this work, we propose a novel virtual-mouse method using RGB-D images and fingertip detection. The hand region of interest and the center of the palm are first extracted using in-depth skeleton-joint information images from a Microsoft Kinect Sensor version 2, and then converted into a binary image. Then, the contours of the hands are extracted and described by a border-tracing algorithm. The K-cosine algorithm is used to detect the fingertip location, based on the hand-contour coordinates. Finally, the fingertip location is mapped to RGB images to control the mouse cursor based on a virtual screen. The system tracks fingertips in real-time at 30 FPS on a desktop computer using a single CPU and Kinect V2. The experimental results showed a high accuracy level; the system can work well in real-world environments with a single CPU. This fingertip-gesture-based interface allows humans to easily interact with computers by hand.

show abstract

“…Iterative techniques were applied to remove the depth image shadow and decrease noise. Bakar et al [15] used fingertips selected using depth threshold and the K-curvature algorithm based on depth data. Wen et.…”

Section: Related Workmentioning

confidence: 99%

Computer Vision for Elderly Care Based on Hand Gestures

Oudah¹,

Al-Naji²,

Chahl³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Finger application using K-Curvature method and Kinect sensor in real-time

Cited by 13 publications

References 13 publications

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Real-time virtual mouse system using RGB-D images and fingertip detection

Computer Vision for Elderly Care Based on Hand Gestures

Contact Info

Product

Resources

About