A temporal Bayesian model for classifying, detecting and localizing activities in video sequences

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One-shot-learning gesture recognition using HOG-HOF features (2013, methodology): “The methods used by other challenge participants — alfnie, Pennect, Joewan (Wan et al., 2013), OneMillionMonkeys, Manavender (Malgireddy et al., 2012) — are summarized by Guyon et al. (2012, 2013).”
Large-scale gesture recognition with a fusion of RGB-D data based on the C3D model (2016, background): “In the early stage, the hand-crafted features like histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are extracted [3] [4] [5], and the finite-state machine (FSM) [6], [7], hidden Markov model (HMM) [5] [8], and dynamic time warping (DTW) [9] are commonly applied in modeling ”
Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, methodology): “For example, HOG and HOF features [19], [26], [27] are also widely used to extract features, and HMM [27] and DTW [20], [28] are commonly applied in temporal segmentation or gesture recognition from RGB-D data.”
One-shot learning gesture recognition from RGB-D data using bag of features (2013, background): “However, in this paper, we explore one-shot learning gesture recognition (Malgireddy et al., 2012), that is, using one training sample per each class.”
Multi-layered gesture recognition with Kinect (2015, background): “Malgireddy et al. (2012) presents an end-to-end temporal Bayesian framework for activity classification.”