Model-based hand tracking using a hierarchical Bayesian filter

By B. Stenger, Arasanathan Thayananthan, Philip H. S. Torr, Roberto Cipolla. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

512 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0132e68e6adf3d087d45f3b1c4803d4140c55e0a

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In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Human-Computer Interaction

Citations in the corpus

Vision-based hand pose estimation: A review (2007, background): “Among different body parts, the hand is the most effective, general-purpose interaction tool due to its dexterous functionality in communication and manipulation.”
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].”
Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data (2013, background): “Other model-based global optimization approaches suffer from the same runtime performance problem [12, 22].”
Multi-view 3D Human Pose Estimation in Complex Environment (2011, methodology): “…and Cipolla 2001; Fossati et al. 2009; Gavrila and Davis 1996; Hasler et al. 2009; Kakadiaris and Metaxas 2000; Lee and Elgammal 2010; Li et al. 2010; Ong et al. 2006; Peursum et al. 2010; Roberts et al. 2006; Rosenhahn and Brox 2007; Stenger et al. 2006; Vondrak et al. 2008; Xu and Li 2007).”
Pixel-Level Hand Detection with Shape-Aware Structured Forests (2014, background): “ensemble of edges [8, 9] from a set of 2D projections of a 3D synthetic hand model.”
Motion Capture of Hands in Action Using Discriminative Salient Points (2012, background): “Most of these methods process the frames independently, however temporal consistency can also be easily enforced [24, 5].”
Pattern Recognition (2015, unknown): “Left : Examples of different species from datasets used in our evaluations showing butterflies [36] (top) and birds [35] (bottom).”
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping (2017, background): “, physics or dynamics [10], multiple hypotheses [21], or analysis-bysynthesis [5].”
Realtime and Robust Hand Tracking from Depth (2014, background): “The global techniques [3, 4] search a large parameter space to avoid poor local optima, but the result is usually coarse and the search is slow.”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “Although these methods process the frames independently, temporal consistency can be enforced [135, 136, 137].”
Human computing and machine understanding of human behavior: a survey (2006, unknown): “[50], [56]).”
Monocular Pedestrian Detection: Survey and Experiments (2009, background): “Efficient matching techniques based on distance-transforms have been combined with precomputed hierarchical structures, to allow for real-time online matching of many thousands of exemplars [22], [23], [67].”
DexPilot: Vision-Based Teleoperation of Dexterous Robotic Hand-Arm System (2019, background): “Through the years, many advancements have been made in this research ﬁeld including the incorporation of haptic feedback [6, 7] and improved human skeletal and ﬁnger tracking [8, 9, 10, 11].”
3D Hand Shape and Pose Estimation From a Single RGB Image (2019, background): “However, despite years of studies [40, 27, 57, 58, 47, 45, 12, 26], it remains an open problem due to the diversity and complexity of hand shape, pose, gesture, occlusion, etc .”
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching (2007, methodology): “Considering previous work, this paper is most related to [27] and [1] in the sense that no constraints are imposed on allowable shapes and in that a hierarchical exemplar representation is combined with a probabilistic matching model.”