Challenges in Multi-modal Gesture Recognition
Sergio Escalera,Vassilis Athitsos,Isabelle Guyon +2 more
- pp 1-60
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TLDR
The state of the art on multimodal gesture recognition is surveyed and the JMLR special topic on gesture recognition 2011–2015 is introduced and several datasets recorded, including tens of thousands of videos, are made available to conduct further research.Abstract:
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect\(^\mathrm{TM}\) revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.read more
Citations
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Vision-based human activity recognition: a survey
TL;DR: Most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks, which establishes new trend and milestone in the development cycle of HAR systems.
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Learning Spatiotemporal Features Using 3DCNN and Convolutional LSTM for Gesture Recognition
TL;DR: Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.
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Sign Language Recognition - A Deep Survey.
TL;DR: A taxonomy to categorize the proposed models for isolated and continuous sign language recognition is presented, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field.
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MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences
TL;DR: An effective multi-dimensional feature learning approach, termed as MultiD-CNN, for human gesture recognition in RGB-D videos is presented, demonstrating that this approach is particularly impressive where it outperforms prior arts in both accuracy and efficiency.
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Survey on Emotional Body Gesture Recognition
Fatemeh Noroozi,Ciprian A. Corneanu,Dorota Kamińska,Tomasz Sapiński,Sergio Escalera,Gholamreza Anbarjafari +5 more
TL;DR: It is shown that for emotion recognition the quantity of labelled data is scarce and there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.
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