A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Sensors, 2019 Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision.
Hong-Bo Zhang +6 more
doaj +3 more sources
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the recent State-Of-The-Art (SOTA) models for this task tends to be exceedingly sophisticated and ...
Yisheng Song +3 more
semanticscholar +1 more source
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition [PDF]
AAAI Conference on Artificial Intelligence, 2021 Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies ...
Zhan Chen +4 more
semanticscholar +1 more source
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition [PDF]
AAAI Conference on Artificial Intelligence, 2018 Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and ...
Sijie Yan, Yuanjun Xiong, Dahua Lin
semanticscholar +1 more source
Action Capsules: Human skeleton action recognition
Computer Vision and Image Understanding, 2023 11 pages, 11 ...
Ali Farajzadeh Bavil +2 more
openaire +2 more sources
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset [PDF]
Computer Vision and Pattern Recognition, 2017 The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-
João Carreira, Andrew Zisserman
semanticscholar +1 more source
A Closer Look at Spatiotemporal Convolutions for Action Recognition [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid ...
Du Tran +5 more
semanticscholar +1 more source
Temporal-Relational CrossTransformers for Few-Shot Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set.
Toby Perrett +4 more
semanticscholar +1 more source
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2020 Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal ...
Ken Ziyu Liu +4 more
semanticscholar +1 more source
PYSKL: Towards Good Practices for Skeleton Action Recognition [PDF]
ACM Multimedia, 2022 We present PYSKL: an open-source toolbox for skeleton-based action recognition based on PyTorch. The toolbox supports a wide variety of skeleton action recognition algorithms, including approaches based on GCN and CNN. In contrast to existing open-source
Haodong Duan +3 more
semanticscholar +1 more source