Results 31 to 40 of about 5,777,447 (338)
Rank-GCN for Robust Action Recognition
IEEE Access, 2022 We present a robust skeleton-based action recognition method with graph convolutional network (GCN) that uses the new adjacency matrix, called Rank-GCN. In Rank-GCN, the biggest change from previous approaches is how the adjacency matrix is generated to ...
Haetsal Lee +3 more
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Towards Understanding Action Recognition [PDF]
2013 IEEE International Conference on Computer Vision, 2013 -
H Jhuang +4 more
openaire +5 more sources
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2023 The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on ...
Lilang Lin, Jiahang Zhang, Jiaying Liu
semanticscholar +1 more source
Darwintrees for Action Recognition [PDF]
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017 We propose a novel mid-level representation for action/activity recognition on RGB videos. We model the evolution of improved dense trajectory features not only for the entire video sequence, but also on subparts of the video. Subparts are obtained using a spectral divisive clustering that yields an unordered binary tree decomposing the entire cloud of
Clapes, Albert +2 more
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View-Invariant Action Recognition [PDF]
, 2020 Human action recognition is an important problem in computer vision. It has a wide range of applications in surveillance, human-computer interaction, augmented reality, video indexing, and retrieval. The varying pattern of spatio-temporal appearance generated by human action is key for identifying the performed action.
Rawat, Yogesh S, Vyas, Shruti
openaire +2 more sources
Viewpoint Manifolds for Action Recognition
EURASIP Journal on Image and Video Processing, 2009 Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are
Richard Souvenir, Kyle Parrigan
doaj +2 more sources
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2023 Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints.
Lei Wang, Piotr Koniusz
semanticscholar +1 more source
Quo Vadis, Skeleton Action Recognition? [PDF]
International Journal of Computer Vision, 2021 In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. We extend our study to include out-of-context
Pranay Gupta +6 more
openaire +2 more sources
IEEE Access, 2021 Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of ...
Matthew S. Hutchinson +1 more
doaj +1 more source
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
Signals, 2021 Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action-related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with
Paritosh Parmar, Brendan Morris
doaj +1 more source