Dual attention based spatial-temporal inference network for volleyball group activity recognition

Li, Yanshan; Liu, Yan; Yu, Rui; Zong, Hailin; Xie, Weixin

doi:10.1007/s11042-022-13867-z

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Sports Action Recognition (SAR) is a challenging task used for various sports, including soccer, volleyball, basketball, tennis, and badminton [1,2,3,4]. SAR detects and recognizes actions during competitions, matches, warm-ups, and training sessions [5,6].…”

Section: Introductionmentioning

confidence: 99%

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Sarwar,

Lin,

Daraghmi

et al. 2023

IEEE Access

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The analysis of badminton player actions from videos plays a crucial role in improving athletes' performance and generating statistical insights. The complexity and speed of badminton movements pose unique challenges compared to everyday activities. To analyze badminton player actions, we propose a skeleton-based keyframe detection framework for action analysis. Keyframe detection is widely used in video summarization and visual localization due to its computational efficiency and memory optimization compared to analyzing all frames of a video. This framework segments the complex macro-level activity into micro-level segments and analyzes each micro-level activity individually. Firstly, it extracts skeleton data from a motion sequence video using 3D:VIBE pose estimation. Then, the keyframe detection module explores the sequence of activity frames and identifies keyframes for each micro-level activity, including start, ready, strike, and end. Finally, the posture and movement detection modules analyze the posture and movement data to identify specific activities. This framework is implemented in the device called CoachBox. The proposed framework is evaluated using the mean absolute error on a dataset. The average mean absolute error for the keyframe detection module is less than 0.168 seconds, and the striking moment detection has an error of only 0.033 seconds. Additionally, a coordinate transform method is provided to convert body coordinates to real-world coordinates for visualization purposes.

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Section: Introductionmentioning

confidence: 99%

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Sarwar,

Lin,

Daraghmi

et al. 2023

IEEE Access

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Rethinking group activity recognition under the open set condition

Zhu,

Wu,

Chang

et al. 2024

Vis Comput

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CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

Zhang,

Chen,

Wang

et al. 2024

Comp. Visual Media

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Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C2 activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks.

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Dual attention based spatial-temporal inference network for volleyball group activity recognition

Cited by 5 publications

References 54 publications

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Rethinking group activity recognition under the open set condition

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

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