FastHand: Fast monocular hand pose estimation on embedded systems
@article{An2021FastHandFM, title={FastHand: Fast monocular hand pose estimation on embedded systems}, author={Shan An and Xiajie Zhang and Dong Wei and Haogang Zhu and Jianyu Yang and Konstantinos A. Tsintotas}, journal={J. Syst. Archit.}, year={2021}, volume={122}, pages={102361} }
Figures and Tables from this paper
2 Citations
VTONShoes: Virtual Try-on of Shoes in Augmented Reality on a Mobile Device
- Computer Science2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
- 2022
This paper proposes an efficient framework to detect, classify, and recover 6-DoF pose of shoes from the captured images and then accurately render the 3D shoe model on the screen in realtime and in full degrees of freedom.
Joint-Aware Action Recognition for Ambient Assisted Living
- Computer Science2022 IEEE International Conference on Imaging Systems and Techniques (IST)
- 2022
This work exploits state-of-the-art data-driven classifiers and compares their efficiency in action recognition based on a specific set of joints or coordinates, i.e., the x, y and z-axis, and investigates the capacity of such a joint analysis and its ability to deliver an enhanced pose-based action recognition system.
References
SHOWING 1-10 OF 45 REFERENCES
SRHandNet: Real-Time 2D Hand Pose Estimation With Simultaneous Region Localization
- Computer ScienceIEEE Transactions on Image Processing
- 2020
A novel method for real-time 2D hand pose estimation from monocular color images, which is named as SRHandNet, to simultaneously regress the hand region of interests and hand keypoints for a given color image, and iteratively take the hand RoIs as feedback information for boosting the performance of hand key points estimation with a single encoder-decoder network architecture.
Attention! A Lightweight 2D Hand Pose Estimation Approach
- Computer ScienceIEEE Sensors Journal
- 2021
This article presents a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module, which can be deployed on an embedded system due to its lightweight nature with just 1.9 Million parameters.
Learning to Estimate 3D Hand Pose from Single RGB Images
- Computer Science2017 IEEE International Conference on Computer Vision (ICCV)
- 2017
A deep network is proposed that learns a network-implicit 3D articulation prior that yields good estimates of the 3D pose from regular RGB images, and a large scale 3D hand pose dataset based on synthetic hand models is introduced.
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB
- Computer Science2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- 2018
This work proposes a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network, and uses a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images.
Hand Pose Estimation via Latent 2.5D Heatmap Regression
- Computer ScienceECCV
- 2018
This paper proposes a new method for 3D hand pose estimation from a monocular image through a novel 2.5D pose representation that implicitly learns depth maps and heatmap distributions with a novel CNN architecture.
3D Hand Pose Estimation from Single Depth Images with Label Distribution Learning
- Computer Science2020 IEEE International Conference on Embedded Software and Systems (ICESS)
- 2020
A deep regression network is proposed, which learns the hand feature space from the point cloud and includes a specific label distribution learning network that achieves the state-of-the-art performance on MSRA dataset.
Robust 3 D Hand Pose Estimation From Single Depth Images Using MultiView CNNs
- Computer Science
- 2018
A novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation that is superior to the state-of-the-art methods on two challenging data sets and validates that the proposed approach has good generalization ability.
Robust 3D Hand Pose Estimation From Single Depth Images Using Multi-View CNNs
- Computer ScienceIEEE Transactions on Image Processing
- 2018
A novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation that is superior to the state-of-the-art methods on two challenging data sets and validates that the proposed approach has good generalization ability.
FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images
- Computer Science, Environmental Science2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2019
This paper introduces the first large-scale, multi-view hand dataset that is accompanied by both 3D hand pose and shape annotations and proposes an iterative, semi-automated `human-in-the-loop' approach, which includes hand fitting optimization to infer both the 3D pose andshape for each sample.