scholarly journals Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

2019 ◽  
Vol 41 (4) ◽  
pp. 956-970 ◽  
Author(s):  
Liuhao Ge ◽  
Hui Liang ◽  
Junsong Yuan ◽  
Daniel Thalmann
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

Graph Convolutional Neural Networks-based 3D Hand Pose Estimation over Point Clouds

2021 ◽  
Author(s):  
John Alejandro Castro-Vargas ◽  
Pablo Martinez-Gonzalez ◽  
Sergiu Oprea ◽  
Alberto Garcia-Garcia ◽  
Sergio Orts-Escolano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Point Clouds ◽  
Hand Pose Estimation ◽  
Hand Pose

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.

HBE: Hand Branch Ensemble Network for Real-Time 3D Hand Pose Estimation

2018 ◽  
pp. 521-536 ◽  
Author(s):  
Yidan Zhou ◽  
Jian Lu ◽  
Kuo Du ◽  
Xiangbo Lin ◽  
Yi Sun ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 585
Author(s):  
Yufei Wu ◽  
Xiaofei Ruan ◽  
Yu Zhang ◽  
Huang Zhou ◽  
Shengyu Du ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Graphics Processing Unit ◽  
Parallel Execution ◽  
Processing Unit ◽  
Network Efficiency ◽  
Hand Pose Estimation ◽  
Central Processing ◽  
Deployment Optimization ◽  
Hand Pose

The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal.

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.

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