A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

By Jonathan Alon, Vassilis Athitsos, Quan Yuan, Stan Sclaroff. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

385 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/d5053c0c676c2597de5f0ca9003fdc2a191700cc

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Citations in the corpus

Survey on 3D Hand Gesture Recognition (2016, background): “Following [23], we can roughly divide continuous hand gestures into direct approaches and indirect approaches depending on whether the explicit temporal segmentation proceeds or not.”
Measuring and reducing observational latency when recognizing actions (2011, background): “For example, as the user transitions from an initial rest state to the climbing action or punching action, the hands will be in a position near the head.”
Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping (2012, methodology): “In spite of some progress from existing ideas such as lower bounding, [3] bemoans DTW is “still too slow for gesture recognition systems”, [29] laments that the “problem of searching with DTW (is) intractable”, [12] says “Clearly (DTW) is unusable for real-time recognition purposes” and [34] notes “”
Activity Recognition on Streaming Sensor Data (2012, methodology): “Template matching techniques employ k-NN classifier on either distance computed between a test window and training windows through Euclidean distance in the case of fixed window size [26] or dynamic time warping in the case of varying window size [27].”
Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition (2014, methodology): “, 2009; Alon et al., 2009; Lee and Kim, 1999), gesture recognition (Starner and Pentland, 1998; Cheng et al., 2012; Wilson and Bobick, 1999; Just and Marcel, 2009; Garg et al., 2009;), sign language recognition (Han et al., 2009; Kong and Ranganath, 2010), and human action recognition (Rao et al., 2”
Online Action Recognition via Nonparametric Incremental Learning (2014, methodology): “As it handles videos on a frame-by-frame basis, the method is suitable to tackle the so-called " continuous action recognition " problem [3] [4] [18] [36] [46] [52], in which both training and test videos contain a number of gestures in a sequence.”
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition (2012, methodology): “A popular strategy for recognizing gestures, used in Alon et al. (2009), and Davis and Tyagi (2006), is based on fitting Hidden Markov Models to different states in the gesture. An advantage of the system proposed in Alon et al. (2009) is that it is also able to spot and temporally segment the actio”
Online human gesture recognition from motion data streams (2013, background): “In most cases, their considerations are to recognize hand gestures online [2, 28] or recognize pre-segmented gesture instances off-line [29].”