A Particle Filtering for 3D Human Hand Tracking

2008 ◽  
Author(s):  
Zhiquan Feng ◽  
Bo Yang ◽  
Yuehui Chen ◽  
Yan Jiang ◽  
Tao Xu ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Hand Tracking ◽  
Human Hand

Identify Finger Rotation Angles With ArUco Markers and Action Cameras

2021 ◽  
Author(s):  
Tianyun Yuan ◽  
Yu (Wolf) Song ◽  
Gerald A. Kraan ◽  
Richard H. M. Goossens
Keyword(s):  
Dynamic Time Warping ◽  
Tracking System ◽  
Low Cost ◽  
Finger Joint ◽  
Hand Tracking ◽  
Human Hand ◽  
Time Warping ◽  
Joint Rotation ◽  
Dynamic Time ◽  
The Right

Abstract Measuring the motion of human hand joints is a challenging task due to the high number of DOFs. In this study, we proposed a low-cost hand tracking system built on action cameras and ArUco markers to measure finger joint rotation angles. The lens distortion of each camera was corrected first via intra-calibration and the videos of different cameras were aligned to the reference camera using a dynamic time warping based method. Two methods were proposed and implemented for extracting the rotation angles of finger joints: one is based on the 3D positions of the markers via inter-calibration between cameras, named pos-based method; the other one is based on the relative marker orientation information from individual cameras, named rot-based method. An experiment was conducted to evaluate the effectiveness of the proposed system. The right hand of a volunteer was included in this practical study, where the movement of the fingers was recorded and the finger rotation angles were calculated with the two proposed methods, respectively. The results indicated that although using the rot-based method may collect less data than using the pos-based method, it was more stable and reliable. Therefore, the rot-based method is recommended for measuring finger joint rotation in practical setups.

Gesture Recognition

2017 ◽  
pp. 191-209
Author(s):  
Nandhini Kesavan ◽  
Raajan N. R.
Keyword(s):  
Gesture Recognition ◽  
Degrees Of Freedom ◽  
Research Work ◽  
Hand Tracking ◽  
Multidimensional Data ◽  
Human Hand ◽  
Challenging Problem ◽  
Hand Gestures ◽  
The Commons ◽  
Video Input

The main objective of gesture recognition is to promote the technology behind the automation of registered gesture with a fusion of multidimensional data in a versatile manner. To achieve this goal, computers should be able to visually recognize hand gestures from video input. However, vision-based hand tracking and gesture recognition is an extremely challenging problem due to the complexity of hand gestures, which are rich in diversities due to high degrees of freedom involved by the human hand. This would make the world a better place with for the commons not only to live in, but also to communicate with ease. This research work would serve as a pharos to researchers in the field of smart vision and would immensely help the society in a versatile manner.

scholarly journals The Dynamic Model Embed in Augmented Graph Cuts for Robust Hand Tracking and Segmentation in Videos

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Wan ◽  
Qiuqi Ruan ◽  
Gaoyun An ◽  
Wei Li ◽  
Yanyan Liang ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Spatial Information ◽  
Graph Cuts ◽  
Hand Tracking ◽  
Human Hand ◽  
Dynamic Background ◽  
Hand Motion ◽  
Hand Segmentation ◽  
New Energy ◽  
Fast Motion

Segmenting human hand is important in computer vision applications, for example, sign language interpretation, human computer interaction, and gesture recognition. However, some serious bottlenecks still exist in hand localization systems such as fast hand motion capture, hand over face, and hand occlusions on which we focus in this paper. We present a novel method for hand tracking and segmentation based on augmented graph cuts and dynamic model. First, an effective dynamic model for state estimation is generated, which correctly predicts the location of hands probably having fast motion or shape deformations. Second, new energy terms are brought into the energy function to develop augmented graph cuts based on some cues, namely, spatial information, hand motion, and chamfer distance. The proposed method successfully achieves hand segmentation even though the hand passes over other skin-colored objects. Some challenging videos are provided in the case of hand over face, hand occlusions, dynamic background, and fast motion. Experimental results demonstrate that the proposed method is much more accurate than other graph cuts-based methods for hand tracking and segmentation.

Identify Finger Rotation Angles with ArUco Markers and Action Cameras

2022 ◽  
pp. 1-25
Author(s):  
Tianyun Yuan ◽  
Yu Song ◽  
Gerald A. Kraan ◽  
Richard HM Goossens
Keyword(s):  
Coordinate System ◽  
Degrees Of Freedom ◽  
Tracking System ◽  
Hand Movements ◽  
Hand Tracking ◽  
Human Hand ◽  
Joint Rotation ◽  
Hand Joints ◽  
Camera Coordinate System ◽  
Joint Experiment

Abstract Measuring the motions of human hand joints is often a challenge due to the high number of degrees of freedom. In this study, we proposed a hand tracking system utilizing action cameras and ArUco markers to continuously measure the rotation angles of hand joints. Three methods were developed to estimate the joint rotation angles. The pos-based method transforms marker positions to a reference coordinate system (RCS) and extracts a hand skeleton to identify the rotation angles. Similarly, the orient-x-based method calculates the rotation angles from the transformed x-orientations of the detected markers in the RCS. In contrast, the orient-mat-based method first identifies the rotation angles in each camera coordinate system using the detected orientations, and then, synthesizes the results regarding each joint. Experiment results indicated that the repeatability errors with one camera regarding different marker sizes were around 2.64 to 27.56 degrees and 0.60 to 2.36 degrees using the marker positions and orientations respectively. When multiple cameras were employed to measure the joint rotation angles, the angles measured by using the three methods were comparable with that measured by a goniometer. Despite larger deviations occurred when using the pos-based method. Further analysis indicated that the results of using the orient-mat-based method can describe more types of joint rotations, and the effectiveness of this method was verified by capturing hand movements of several participants. Thus it is recommended for measuring joint rotation angles in practical setups.

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