Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition

By Ming-Hsuan Yang, Narendra Ahuja, Mark Tabb. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

391 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/001c353521cd29abc0cac59141ccc29d843dd9b8

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Sign Language Recognition (2011, methodology): “[109] presented a general method to extract motion trajectories, and then used them within a Time Delay Neural Network (TDNN) to recognise ASL.”
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning (2005, background): “If skin-color detection is used, the signer is often required to wear long-sleeved clothing, with restrictions on other skin-colored objects in the background ([1], [67], [68], [120], [123], [158], [162]).”
A system for learning statistical motion patterns (2006, methodology): “TDNN has been successfully applied to hand gesture recognition [50] and lip-reading [29].”
One-shot learning gesture recognition from RGB-D data using bag of features (2013, background): “Simple descriptors, such as motion trajectories (Yang et al., 2002) and spatio-temporal gradients (Freeman and Roth, 1995), may not meet the invariant conditions.”
Recent methods and databases in vision-based hand gesture recognition: A review (2015, methodology): “[32] 2D motion field/trajectory NN American sign language”
Recognizing human actions in videos acquired by uncalibrated moving cameras (2005, methodology): “[5] also used the affine transformation computed between the corresponding segments in consecutive frames for sign language recognition.”
Challenges in multimodal gesture recognition (2016, methodology): “Oftentimes, the extracted features describe the position and appearance of the gesturing hand or hands (Cutler and Turk, 1998; Darrell et al., 1996; Starner and Pentland, 1998; Yang et al., 2002)).”
Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera (2015, methodology): “Methods based on skin color model [6] and hand shape model [7] have also been proposed.”
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation (2009, background): “In many dynamic gesture recognition systems (e.g., [1], [2], [3], [4], [5], [6], [7]), lower level modules perform spatial and temporal segmentations and extract shape and motion features.”