Challenges in multimodal gesture recognition

By Sergio Escalera, Vassilis Athitsos, Isabelle M Guyon. 2016

76 citations recorded by the source corpus.

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

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Citations in the corpus

RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Several survey papers have summarized the research on human motion recognition using RGB-D data (Aggarwal and Xia, 2014; Chen et al., 2013; Cheng et al., 2016; Escalera et al., 2016; Han et al., 2017; Presti and La Cascia, 2016; Ye et al., 2013; Zhang et al., 2016b).”
ChaLearn looking at people: A review of events and resources (2017, background): “Topic at Journal of Machine Learning Research [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30] 2016 TPAMI HuPBA: We organized a Special Issue on Multi-modal Human Pose Recovery and Behavior Analysis (HuPBA) at IEEE Transactions on Pattern Ana”
Vision-based human activity recognition: a survey (2020, background): “Ontheother hand, reviews of vision-based hand gesture recognition methods were presented in [33, 34, 45, 64, 119, 145, 153, 227].”
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 (2016, methodology): “Following previous series on Looking at People (LAP) competitions [14, 13, 9, 12, 4, 10, 42, 15], in 2016, ChaLearn and Faces of the World organized new competitions and workshops on automatic face analysis in still images.”
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, background): “The challenge of action recognition is mainly related to the difficulty of extracting body silhouettes from foreground rigid objects to focus on their emotions [96].”
ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition (2016, background): “The field of Looking at People recently has received special attention, and several datasets have been also presented in order to deal with different computer vision image analysis tasks, such as human pose recovery, action and gesture recognition, and face analysis, just to mention a few [7, 6, 3, ”
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016, background): “Gestures are naturally performed by humans, produced as part of deliberate actions, signs or signals, or subconsciously revealing intentions or attitude [1].”
Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks (2016, background): “Kinect revolution makes inexpensive infrared cameras providing image depth information available, thus multimodalities (including RGB, Depth, Optical Flow, Skeleton) based gesture recognition methods emerge [4, 18] after that.”
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM (2017, background): “Convolutional neural networks (CNN) and RNN based methods have demonstrated the state-of-the-art performances on human gesture/action recognition [6], [32], [33].”