Real-time Hand Detection Research Articles

Virtual Air Canvas: A Review

This creative method of utilizing open-source frameworks to create gesture-controlled whiteboard devices is impressive. The device tracks and interprets finger movements collected by a camera to allow users to engage with a virtual whiteboard. The implementation includes effective data processing, hand landmark estimate, and real-time hand detection by utilizing computer vision algorithms. By providing a modern form of communication and going beyond regular typing and writing habits, the system seeks to expand beyond traditional writing methods and create new opportunities for connection. The system contributes to the changing field of human-computer interaction by offering an alternate mode of communication, which improves people's quality of life overall. It also demonstrates the versatility of gesture-controlled systems in terms of enhancing communication accessibility. Key Words: Data Processing, Handmark Estimation, Computer Vision algorithms, MediaPipe, OpenCV.

Keyboard and Mouse Free Music Controller

Abstract: Human-machine interface (HMI) is a crucial area of research as gestures have the potential to efficiently control and interact with computers. Many applications for hand detection have been created as a result of the pervasive use of built-in cameras in computers, smartphones, and tablets. For the majority of users, however, many of these are not useful. A straightforward concept for a keyboard- and mouse-free music controller is presented in this research. Using MATLAB code that integrates skin detection, area labelling, erosion, dilation, and motion differentiation, a music player controller is developed using the real-time frame tracking feature of a camera. Three hand detection algorithms are created and assessed for maximum performance and accuracy. Real-time hand detection for operating the music player is provided by the algorithm, which is created with efficiency and speed in mind.

Multi-fusion feature pyramid for real-time hand detection

Multi-fusion feature pyramid for real-time hand detection

Detection of Hands for Hand-Controlled Skyfall Game in Real Time Using CNN

With human-computer interaction technology evolving, direct use of the hand as an input device is of wide attraction. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection. This paper focuses on creating a hand-controlled web-based skyfall game by building a real time hand detection using CNN-based technique. A CNN network, which uses a MobileNet as the feature extractor along with the single shot detector framework, is used to achieve a robust and fast detection of hand location and tracking. Along with detection and tracking of hand, skyfall game has been designed to play using hand in real time with tensor flow framework. This way of designing the game where hand is used as input to control the paddle of skyfall game improved the player interaction and interest towards playing the game. This model of CNN network used egohands dataset for detecting and tracking the hands in real time and produced an average accuracy of 0.9 for open hands and 0.6 for closed hands which in turn improved player and game interactions.

Robust real-time hand detection and localization for space human–robot interaction based on deep learning

Hand gestures are quite suitable for space human–robot interaction (SHRI) because of their natural and convenient features. While the detection and localization of hands are the premise and foundation for SHRI based on hand gestures. But hand gestures are very complicated and hand sizes are very small in some images. These problems make the robust real-time hand detection and localization very difficult. In this paper, a feature-map-fused single shot multibox detector (FF-SSD) which is a deep learning network is designed to deal with the problems of hand detection and localization in SHRI. First, the background of the method is introduced in this paper, including an astronaut assistant robot platform, the difficulties of hand detection and localization, and introduction of the state-of-the-art deep learning networks for object detection and localization. Then, the FF-SSD is proposed for detecting and localizing hands especially pony-size hands. This network takes into consideration both accuracy and speed with balanced performance. And in the experiment part, the FF-SSD is trained and tested on hand databases which include a homemade database and two public databases. At last, the superiority of the proposed method is demonstrated compared with the state-of-the-art methods.

Real-time hand detection using continuous skeletons

In this paper, a fast and reliable method for hand detection based on continuous skeletons approach is presented. It demonstrates real-time working speed and high detection accuracy (3---5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). These make it suitable for use as a part of modern hand identification systems including mobile ones. Overall, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g., interactive segmentation with seeds or abounding box).

A Real-Time Hand Detection System during Hand over Face Occlusion

This paper devises a vision-based hand detection system which can handle the situation of the hand and face occlusion. Firstly, human face is coarsely located by skin color detection and ellipse template matching and further determined by using the grayscales and positions of human eyes. Secondly, when the hand occludes the partial region of the face, a novel hand detection method based on Camshift tracking algorithm, force field method and Sobel edge extraction is developed to segment the hand. Finally, the positions of segmented hands are sent to the computer for controlling the cursor movement. In order to reduce the cursor jitter caused by wrong hand detection and generate a smooth trajectory of the cursor, the coordinates of hand positions are corrected by combining the least squares fitting method of orthogonal polynomial and an adaptive Catmull-Rom interpolation algorithm. Experimental results showed that the proposed method could detect hands accurately with the run time of 0.08s per frame and demonstrated a significant improvement in performance when the hand enters the face region. Moreover, our system accomplished smooth movements of the cursor by the vision-based hand detection.

A real-time hand detection system based on multi-feature

This paper describes a real-time hand detection system which can reach high speed and accuracy. The system is based on Gentle Adaboost and cascade classifier. To improve the performance of the system, three efficient features are selected to describe the visual properties of human hands. In addition, the detection is accelerated due to several optimization methods, including the method for fast calculation of HOG features, improved cascade classifier and skin-color pre-detection. Experiments were performed on our self-constructed dataset, the results showed that the detection rate of the system can reach 0.889 while the false rate is 0.010 at the speed of 32.6339ms per frame on a Intel Core i5-2400 CPU running at 3.1GHz.

Real time hand detection in a complex background

Hand gesture recognition has gained the interest of many researchers in recent years, as it has become one of the most popular Human Computer Interfaces. The first step in most vision-based gesture recognition systems is the hand region detection and segmentation. This segmentation can be a particularly challenging task when it comes to complex backgrounds and varying illumination. In such environments, most hand detection techniques fail to obtain the exact region of the hand shape, especially in cases of dynamic gestures. Meeting these requirements becomes even more difficult, due to real-time operation demand. To overcome these problems, in this paper, we propose a new method for real-time hand detection in a complex background. We employ a combination of existing techniques, based on motion detection and introduce a novel skin color classifier to improve segmentation accuracy. Motion detection is based on image differencing and background subtraction. Skin color detection is accomplished via a color classification technique that employs online color training, so that the system can dynamically adapt to the variety of lighting conditions and the user׳s skin color as well as possible. Morphological features of the detected hand in previous frames are employed to estimate the probability of a pixel belonging to the hand section in the current frame. Finally, the derived motion, color and morphological information are combined to detect the hand region. Experimental results show significant improvement in hand region detection, compared to existing methods with an average accuracy of 98.75%.

A Research on Real-Time Hand-Detection Technology Oriented to the Somatic Games

Hand-detection is a key technology to the somatic games. In this paper, we present a real-time hand-detection method based on Adaboost and skin-color characteristic. By processing the video frames with Adaboost classifier, we abstract the target regions which may contain the hand gestures. Then a filter based on skin color is proposed to select the correct regions. The best detection rate reaches above 89% with an acceptable failure rate and misjudgment rate. Experimental results show that this method is a lightweight and rapid approach to implement real-time hand detection in somatic games.