Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests

By Danhang Tang, Tsz-Ho Yu, Tae-Kyun Kim. 2013

236 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection (2016, methodology): “Motivated by this, we consider decision forests [3], which have been widely adopted in computer vision [22, 24, 33], owing to many desired properties: clusters obtained in leaf nodes, scalability, robustness to overfitting, multiclass learning and efficiency.”
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (2017, background): “The field of 3D hand pose estimation has advanced rapidly, both in terms of accuracy [2, 4, 5, 6, 7, 26, 28, 32, 38, 45, 48, 49, 53, 54, 57, 60, 62] and dataset quality [10, 22, 43, 47, 52, 59].”
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 ModDrop (2018, unknown): “A group of recent works is dedicated to inferring the hand pose through pixel-wise hand segmentation and estimating the positions of hand or body joints in a bottom-up fashion [20], [21], [22], [23], [24].”
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images (2014, background): “…reveals that our approach achieves a body part detection accuracy on a par with the state-of-the-art on the sports dataset and greatly outperforms [5] in terms of joint localization accuracy on the FashionPose dataset, while achieving a run time of a couple of images per second on the LSP dataset.”
Real-Time Online Action Detection Forests Using Spatio-Temporal Contexts (2016, methodology): “Recently, it has been demonstrated that for RFs, multiple heterogeneous split objectives can be straightforwardly incorporated, even without having to introduce the weighting hyper-parameters, by randomly sampling a sub-split objective at each splitting process [9, 41, 48].”
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations (2017, background): “3D hand pose estimation: Mainly due to the recent availability of RGB-D sensors, the field has made significant progress in object-less third-person view [22, 25, 28, 37, 39, 41, 45, 53, 60, 80] and more modest advances in first-person view [10, 35, 38, 48].”
Training a Feedback Loop for Hand Pose Estimation (2015, methodology): “A popular approach is to use a discriminative method to predict the position of the joints [13, 21, 30, 31, 35], because they are now robust and fast.”
Accurate, Robust, and Flexible Real-time Hand Tracking (2015, background): “[27, 26] extend this work demonstrating more complex poses at 25Hz.”
Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation (2016, background): “In [5], [31], and [32], a random forest classifier was trained from a large dataset to label depth pixels as body/hand parts.”
Pattern Recognition (2015, unknown): “, [2,4,5,8,26,27,34,37], could greatly assist in this process by suggesting visually similar genera for further inspection to an expert.”
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation (2016, background): “Many prior works have achieved good performance by different methods [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].”
Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs (2016, methodology): “Index Terms—3D hand pose estimation, Convolutional neural networks, Multi-view CNNs F”
Hands Deep in Deep Learning for Hand Pose Estimation (2015, background): “[23] proposed a semi-supervised regression forest, which first classifies the hands viewpoint, then the individual joints, to finally predict the 3D joint locations.”
Hand Pose Estimation through Weakly-Supervised Learning of a Rich Intermediate Representation (2015, methodology): “In settings where the user is not directly placed in front of the computer, and therefore not close to the camera, the problem is inherently difficult.”
Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture (2014, methodology): “Tang et al. [17] investigate semisupervised learning for hand pose estimation using annotated synthetic data and unlabelled real data.”