A Comprehensive Survey of Vision-Based Human Action Recognition Methods.
TLDR
This survey paper provides a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods.Abstract:
Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human⁻object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.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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Synthetic Data for Deep Learning
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Skeleton-based action recognition via spatial and temporal transformer networks
TL;DR: This work proposes a novel Spatial-Temporal Transformer network (ST-TR) which models dependencies between joints using the Transformer self-attention operator, outperforming the state-of-the-art on NTU-RGB+D w.r.t.
Book ChapterDOI
Spatial Temporal Transformer Network for Skeleton-based Action Recognition
TL;DR: This work proposes a novel Spatial-Temporal Transformer network (ST-TR) which models dependencies between joints using the Transformer self-attention operator, outperforming the state-of-the-art on NTU-RGB+D w.r.t. models using the same input data consisting of joint information.
Journal ArticleDOI
Vision-based human action recognition: An overview and real world challenges
TL;DR: This paper investigates an overview of the existing methods according to the kind of issue they address, and presents a comparison of the already introduced datasets introduced for the human action recognition field.
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UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
TL;DR: This work introduces UCF101 which is currently the largest dataset of human actions and provides baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%.
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Long-term Recurrent Convolutional Networks for Visual Recognition and Description
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