Self-Attention Network for Skeleton-based Human Action Recognition

Cho, Sangwoo; Maqbool, M.H.; Liu, Fei; Foroosh, Hassan

doi:10.1109/wacv45572.2020.9093639

Cited by 91 publications

(40 citation statements)

References 25 publications

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“…Self-Attention Network. There are 03 essential forms of the self-attention-network (SAN) these are SAN-Version 1 (V1), SAN-Version 2 (V2) & SAN-Version 3 (V3) [48] . The importance of the investigations are-03 SAN version, which is used for important correlations to form deep semantics, and accumulated Temporal Segment Network (TSN) thru SAN deviations by which performance can be measured.…”

Section: Motif-based Spatial Temporal Graph Convolutional Network (Motif-stgcn)mentioning

confidence: 99%

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Remarkable Skeleton Based Human Action Recognition

Jaiswal

2020

Artificial Intelligence Evolution

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Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance. However, the most challenging and crucial task of the skeleton-based human-action-recognition (SBHAR) is a significant view variation while capturing the data. In this area, a significant amount of satisfactory work has already been done, which include the Red Green Blue (RGB) data method. The performance of the SBHAR is also affected by the various factors such as video frame setting, view variations in motion, different backgrounds and inter-personal differences. In this survey, we explicitly address these challenges and provide a complete overview of advancement in this field. The deep learning method has been used in this field for a long time, but so far, no research has fully demonstrated its usefulness. In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e. NTU-RGB+D and its new version NTU-RGB+D 120.

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Section: Motif-based Spatial Temporal Graph Convolutional Network (Motif-stgcn)mentioning

confidence: 99%

“…Figure 3. Class-score-fusion structure [48] Beyond Joints. The authors [49] worked on 03 primary factors such as joints, edges and surfaces.…”

Section: Motif-based Spatial Temporal Graph Convolutional Network (Motif-stgcn)mentioning

confidence: 99%

Remarkable Skeleton Based Human Action Recognition

Jaiswal

2020

Artificial Intelligence Evolution

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“…Human actions are composed of contemporary behaviors of human body parts. The objective of human action recognition is to recognize actions automatically from an unlabeled video [ 4 , 5 ]. To capture human actions, there are two broad categories of devices based on wearable sensors and video sensors.…”

Section: Introductionmentioning

confidence: 99%

Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Bulbul¹,

Tabussum²,

Ali

et al. 2021

Sensors

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This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.

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“…Neural Network (RNN). Most existing CNN-based works [4,5,6,7] encode the skeleton sequence as an image with the image fed into a CNN-based model to extract features.…”

Section: Introductionmentioning

confidence: 99%

Spatial Reasoning and Context-Aware Attention Network for Skeleton-Based Action Recognition

You

Han

Liang

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Skeleton-based action recognition has achieved promising performance recently, but there are still many challenges, e.g., the structural relation between joints and the different attention of frames, due to the complex spatial-temporal evolution of skeletal joints. In this paper, we propose a spatial reasoning and context-aware attention network for skeleton-based action recognition, which consists of a spatial reasoning module and a context-aware attention module. The spatial reasoning module can exploit the structural relation between joints to obtain the spatial features within each skeleton frame, followed by the context-aware attention module learning the different attention of frames. We perform experiments on two datasets and verify the effectiveness of each module in the proposed network. The comparison results demonstrate that our method achieves state-of-the-art performance.

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Self-Attention Network for Skeleton-based Human Action Recognition

Cited by 91 publications

References 25 publications

Remarkable Skeleton Based Human Action Recognition

Remarkable Skeleton Based Human Action Recognition

Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Spatial Reasoning and Context-Aware Attention Network for Skeleton-Based Action Recognition

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