Real-time hand-tracking with a color glove

By Robert Y. Wang, Jovan Popovic. ACM Transactions on Graphics, 2009

813 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f35a45cd6aa6cf4f342073243d2be132adb571da

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

In Human-Computer Interaction

Citations in the corpus

Cascaded hand pose regression (2015, background): “Model based pose tracking There are a lot of model based approaches for hand tracking [30, 14, 15, 16, 32, 23, 37, 26, 25, 31] and human body tracking [1, 19, 36].”
Efficient Human Pose Estimation from Single Depth Images (2013, background): “Traditional human body tracking algorithms [8], [9], [1], [10], [6], [11] infer a complete skeleton by exploiting kinematic constraints and can achieve high frame-rates by using temporal coherence from frame-to-frame.”
A data-driven approach for real-time full body pose reconstruction from a depth camera (2011, methodology): “To cope with global rotations, one could augment the database to contain pose representations from several viewing directions [6, 26, 29].”
Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests (2013, methodology): “Recent discriminative algorithms for hand pose estimation include approximate nearest neighbour search [23, 27] and hierarchical random forests [16].”
Efficient model-based 3D tracking of hand articulations using Kinect (2011, methodology): “To ease some of these problems, some very successful methods employ specialized hardware for motion capture [21] or the use of visual markers as in [25].”
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language (2018, background): “Most of the proposed approaches rely on external devices such as additional RGB [4] or depth cameras [63, 71], sensor [40, 44] or colored [68] gloves.”
Hand Pose Estimation via Latent 2.5D Heatmap Regression (2018, neutral): “To alleviate these challenges, many proposed solutions simplify the problem by using calibrated multi-view camera systems [1–9], depth sensors [10–24], or color markers/gloves [25].”
Parsing the Hand in Depth Images (2014, methodology): “In [14] a color glove with specially designed patterns is used to segment each hand part.”
Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects (2012, background): “This, however, prohibits the use of mobile devices and introduces additional cost and complexity [20, 40].”
Soli (2016, neutral): “Computer vision techniques have achieved great progress over the last decade [Kim et al. 2012] but still suffer from latency and difficulty resolving overlapped fingers and objects, requiring instrumentation of human hands [Wang and Popović 2009; Dorfmuller-Ulhaas and Schmalstieg 2001].”
Motion Capture of Hands in Action Using Discriminative Salient Points (2012, background): “While intrusive methods like data gloves or color gloves [1] provide an acceptable solution for some applications, marker-less hand tracking is still an unsolved problem.”
3D Gestural Interaction: The State of the Field (2013, background): “For example, Wang and Popović [24] designed a colored glove with a known pattern to support a nearest-neighbor approach to tracking hands at interactive rates.”
Survey on 3D Hand Gesture Recognition (2016, methodology): “gloves have been employed to simplify the vision tasks [12].”
First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset (2020, background): “Therefore, understanding hands with computer vision systems has been deeply explored through hand tracking [41, 54], hand pose estimation [18, 21, 22, 27, 29], grasp detection [10, 47], hand gesture recognition [58], multi-view prediction [29], and hand-action classiﬁcation [50].”
American sign language recognition with the kinect (2011, methodology): “Most previous works either use appearance based methods [1] or use some form of nearest neighbor matching in a database of poses [28] to determine the hand pose.”
Accurate, Robust, and Flexible Real-time Hand Tracking (2015, methodology): “The 3Gear Systems [1] (based on prior work of [32, 31]) and the secondgeneration software for the Leap Motion [15] have shown tracking of a range of complex poses, including two-handed interaction.”