3D hand pose estimation and classification using depth sensors

2012 ◽  
Author(s):  
Cem Keskin ◽  
Furkan Kirac ◽  
Yunus Emre Kara ◽  
Lale Akarun
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Depth Sensors ◽  
Hand Pose

scholarly journals CrossFuNet: RGB and Depth Cross-Fusion Network for Hand Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6095
Author(s):  
Xiaojing Sun ◽  
Bin Wang ◽  
Longxiang Huang ◽  
Qian Zhang ◽  
Sulei Zhu ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Depth Map ◽  
Depth Information ◽  
Feature Maps ◽  
Hand Pose Estimation ◽  
Depth Sensors ◽  
Key Points ◽  
Rgb Images ◽  
Public Datasets ◽  
Hand Pose

Despite recent successes in hand pose estimation from RGB images or depth maps, inherent challenges remain. RGB-based methods suffer from heavy self-occlusions and depth ambiguity. Depth sensors rely heavily on distance and can only be used indoors, thus there are many limitations to the practical application of depth-based methods. The aforementioned challenges have inspired us to combine the two modalities to offset the shortcomings of the other. In this paper, we propose a novel RGB and depth information fusion network to improve the accuracy of 3D hand pose estimation, which is called CrossFuNet. Specifically, the RGB image and the paired depth map are input into two different subnetworks, respectively. The feature maps are fused in the fusion module in which we propose a completely new approach to combine the information from the two modalities. Then, the common method is used to regress the 3D key-points by heatmaps. We validate our model on two public datasets and the results reveal that our model outperforms the state-of-the-art methods.

Real Time Hand Pose Estimation Using Depth Sensors

2013 ◽  
pp. 119-137 ◽  
Author(s):  
Cem Keskin ◽  
Furkan Kıraç ◽  
Yunus Emre Kara ◽  
Lale Akarun
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Depth Sensors ◽  
Hand Pose

scholarly journals Hand Pose Estimation in the Task of Egocentric Actions

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 10533-10547
Author(s):  
Marek Hruz ◽  
Jakub Kanis ◽  
Zdenek Krnoul
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals 3D Hand Pose Estimation via Graph-Based Reasoning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35824-35833
Author(s):  
Jae-Hun Song ◽  
Suk-Ju Kang
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

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