Real-Time Hand Detection by Depth Images: A Survey (original) (raw)

Real-Time Hands Detection in Depth Image by Using Distance with Kinect Camera

Hand detection is one of the most important and crucial step in human computer interaction environment. This paper presents a distance technique for hand detection based on depth image information get from Kinect sensor. Distance was used as the method of this study for hand detection. First, Kinect sensor was used to obtain depth image information. Second, background subtraction and iterative method for shadow removal applied to reduce noise from the depth image. Then, it used the official Microsoft SDK for extraction process. Finally, two hands could be segmented based on different color in specific distance. The experiment result shows that hands and head can be detected in a different position with good accuracy.

Real-Time Hand Detection and Tracking with Depth Values

2015 International Conference on Advances in Electrical Engineering (ICAEE), 2015

Aim of this paper is to propose a methodology of real-time hand detection based on skin color model and background subtraction under any complex background with extracting the depth information. With the use of stereo camera calibration and disparity mapping, the depth information of the hand is extracted. Reasonable selection of threshold of skin color model and combining with background difference segmentation, then a bitwise-and operation results hand’s binary outline feature clearly. Combining this result with the distance information experiments show accurate detection and tracking of hand with depth measuring from the camera.

Hand Detection and Tracking Using Depth and Color Information

The detection and tracking of a hand is an emerging research issue now-a-days to control the devices by hand motion. Conventional hand detection methods use color and shape information from a RGB camera. With the recent advent of the depth camera, some researchers show that they can improve the performance of hand detection by combining the color (or intensity) information with the information from the depth camera. In this paper, we propose a novel method for hand detection using both color and depth information from Microsoft’s Kinect device. The proposed method extract the candidate hand regions from the depth image and select the best candidate based on the color and shape feature of each candidate regions. Then the contour of the selected candidate is determined in the higher resolution RGB image to improve the positional accuracy. For the tracking of the detected hand, we propose the boundary tracking method based on Generalized Hough Transform (GHT). The experimental results show that proposed method can improve the accuracy of hand motion detection over conventional methods.

A New Approach For Hand Gestures Recognition Based on Depth Map Captured by RGB-D Camera

Computación y Sistemas, 2016

This paper introduces a new approach for hand gesture recognition based on depth Map captured by an RGB-D Kinect camera. Although this camera provides two types of information "Depth Map" and "RGB Image", only the depth data information is used to analyze and recognize the hand gestures. Given the complexity of this task, a new method based on edge detection is proposed to eliminate the noise and segment the hand. Moreover, new descriptors are introduce to model the hand gesture. These features are invariant to scale, rotation and translation. Our approach is applied on French sign language alphabet to show its effectiveness and evaluate the robustness of the proposed descriptors. The experimental results clearly show that the proposed system is very satisfactory as it to recognizes the French alphabet sign with an accuracy of more than 93%. Our approach is also applied to a public dataset in order to be compared in the existing studies. The results prove that our system can outperform previous methods using the same dataset.

Real-Time 3D Hand Gesture Detection from Depth Images

Advanced Materials Research, 2013

In this paper, we describe an real-time algorithm to detect 3D hand gestures from depth images. Firstly, we detect moving regions by frame difference; then, regions are refined by removing small regions and boundary regions; finally, foremost region is selected and its trajectories are classified using an automatic state machine. Experiments on Microsoft Kinect for Xbox captured sequences show the effectiveness and efficiency of our system.

Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame

2012

This paper describes a method that, given an input image of a person signing a gesture in a cluttered scene, locates the gesturing arm, automatically detects and segments the hand and finally creates a ranked list of possible shape class, 3D pose orientation and full hand configuration parameters. The clutter-tolerant hand segmentation algorithm is based on depth data from a single image captured with a commercially available depth sensor, namely the Kinect TM.

Using Depth Cameras for Recognition and Segmentation of Hand Gestures

Advances in Materials Science and Engineering, 2021

In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of archit...

Real time hand detection in a complex background

Engineering Applications of Artificial Intelligence, 2014

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%.

Hand segmentation for hand-object interaction from depth map

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017

Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.

Depth-based hand gesture recognition

Multimedia Tools and Applications, 2015

In this article, a dynamic gesture recognition system with the depth information is proposed. The proposed system consists of three main components: preprocessing, static posture recognition and dynamic gesture recognition. In the first component, the background subtraction is used to exclude invalid gestures that is not generated by the main user, and then to detect and track the hand. Second, the region of hand is extracted using an adaptive square. Once the region of hand is obtained, the features of hand are extracted and the static hand posture are classified using the support vector machine (SVM). Finally, nine commonly used dynamic hand gestures can be detected using different methods. In the experiments, the static hand posture classification was evaluated in different postures and the performance of dynamic gesture recognition is verified by two different persons at 4 different position with 2 different depths. The experiment results show that the proposed system can accurately detect the dynamic hand gestures with an average recognition rate of 87.6 %, which is good for controlling the embedded systems, such as home appliances. Keyword Depth cameras. Static hand posture recognition. Dynamic hand gesture recognition. Support vector machine Multimed Tools Appl