Hand Pose Estimation and Hand Shape Classification Using Multi-layered Randomized Decision Forests

By Cem Keskin, Mustafa Furkan Kıraç, Yunus Emre Kara, Lale Akarun. 2012

322 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

3D Hand Shape and Pose From Images in the Wild (2019, background): “There is a rich literature on 3D hand pose and reconstruction from depth [62, 66, 10, 36, 61, 11, 43, 45, 19, 20, 24, 30, 37, 48, 52, 53, 59, 64], image and depth [26, 32, 49, 28], stereo [33, 65, 41] and multiple images [8, 31, 50].”
Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks (2014, methodology): “Osadchy et al. [2005] use a similar formulation to perform pose detection of faces via a nonlinear mapping to a low-dimensional manifold.”
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”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Human hand pose estimation empowers many practical applications, for example sign language recognition [14], visual interfaces [17], and driver analysis [20].”
Parsing the Hand in Depth Images (2014, methodology): “We also implement the Multi-layered Random Decision Forests [28] to parse the hand based on the 1520-9210 (c) 2013 IEEE.”
Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture (2014, methodology): “Note that here both our method and [8] are unoptimised—single threaded, without any CPU/GPU parallelism.”
Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data (2013, methodology): “[10] proposed a method for hand pose estimation by hand part labeling but not as a kinematic skeleton.”
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation (2015, background): “Some approaches also segment the body parts first and estimate the pose in a second step (Keskin et al, 2012; Tompson et al, 2014).”
HOnnotate: A Method for 3D Annotation of Hand and Object Poses (2019, background): “Recent works based on Deep Networks [15,32,34,35,59,69,73] show remarkable performance, compared to early works based on Random Forests such as [23].”
Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images (2018, background): “Although 3D hand pose estimation with depth cameras [13, 7, 26, 41, 6] has gained tremendous success in recent years, the advance in monocular RGB-based 3D hand pose estimation[46, 18, 27, 15], however, still remains limited.”
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].”
Training a Feedback Loop for Hand Pose Estimation (2015, background): “Accurate hand pose estimation is an important requirement for many Human Computer Interaction or Augmented Reality tasks [10], and has been steadily regaining ground as a focus of research interest in the past few years [13, 14, 19, 23, 26, 30, 31, 37], probably because of the emergence of 3D sensor”
Accurate, Robust, and Flexible Real-time Hand Tracking (2015, methodology): “Existing approaches include multi-layer random forests [12], nearest-neighbor look-up [31], fingertip detection [20], or convolutional neural networks [30].”
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.”
Challenges in multimodal gesture recognition (2016, background): “Most recently, due to the advent of commercially available depth sensors, there is an increased interest in methods relying on depth data (Keskin et al., 2012; Mo and Neumann, 2006; Oikonomidis et al., 2011; Pugeault and Bowden, 2011; Lopes et al., 2014).”