scholarly journals Grasping Hand Pose Estimation from RGB Images Using Digital Human Model by Convolutional Neural Network

2018 ◽  
Author(s):  
Kentaro INO ◽  
Naoto IENAGA ◽  
Yuta SUGIURA ◽  
Hideo SAITO ◽  
Natsuki MIYATA ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Human Model ◽  
Hand Pose Estimation ◽  
Digital Human Model ◽  
Rgb Images ◽  
Digital Human ◽  
Hand Pose

scholarly journals Real-Time Energy Efficient Hand Pose Estimation: A Case Study

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2828
Author(s):  
Mhd Rashed Al Koutayni ◽  
Vladimir Rybalkin ◽  
Jameel Malik ◽  
Ahmed Elhayek ◽  
Christian Weis ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Energy Efficient ◽  
Graphics Processing Units ◽  
Estimation Algorithm ◽  
High Energy ◽  
Estimation Methods ◽  
Hand Pose Estimation ◽  
Hand Pose

The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.

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.

scholarly journals InterNet+: A Light Network for Hand Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6747
Author(s):  
Yang Liu ◽  
Jie Jiang ◽  
Jiahao Sun ◽  
Xianghan Wang
Keyword(s):  
Performance Improvement ◽  
Pose Estimation ◽  
Network Architecture ◽  
Activation Function ◽  
Depth Information ◽  
Global Features ◽  
Hand Pose Estimation ◽  
Feature Extractor ◽  
Rgb Images ◽  
Hand Pose

Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement. Based on the architecture of MobileNet v3 and MoGA, we redesigned a feature extractor that introduced the latest achievements in the field of computer vision, such as the ACON activation function and the new attention mechanism module, etc. Using these modules effectively with our network, architecture can better extract global features from an RGB image of the hand, leading to a greater performance improvement compared to InterNet and other similar networks.

3A1-V03 Hand pose estimation using orientation histograms(Digital Human)

2014 ◽  
Vol 2014 (0) ◽  
pp. _3A1-V03_1-_3A1-V03_2
Author(s):  
Hayato MURAI ◽  
Mitsunori TADA ◽  
Gakuto MASUYAMA ◽  
Kazunori UMEDA
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Digital Human ◽  
Hand Pose

Multi-Sensor Motion Fusion Using Deep Neural Network Learning

2017 ◽  
Vol 8 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Xinyao Sun ◽  
Anup Basu ◽  
Irene Cheng
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Rapid Development ◽  
Neural Network Learning ◽  
Input Devices ◽  
Hand Pose Estimation ◽  
Temporal Features ◽  
Deep Recurrent Neural Network ◽  
Real Time Application ◽  
Hand Pose

Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.

Hierarchical neural network for hand pose estimation

2020 ◽  
Vol 87 ◽  
pp. 115909
Author(s):  
Zheng Chen ◽  
Kuo Du ◽  
Yi Sun ◽  
Xiangbo Lin ◽  
Xiaohong Ma
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hierarchical Neural Network ◽  
Hand Pose
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