Deep Spatial/temporal-level feature engineering for Tennis-based action recognition

Bai, Ning; Na, Liu

doi:10.1016/j.future.2021.06.022

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…(1) e motion videos collected in this paper are all shot in front, and the subjects are always kept in the picture. (2) ere is almost no extra object shielding the human body and background interference in the video scene. is results in high integrity and accuracy of extracted bone data, which is very important for motion recognition.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…With the rapid development of artificial intelligence, image pattern recognition technology has played an essential role in People's Daily life in recent years [ 1 ]. Human motion recognition models spatiotemporal information based on presegmented temporal sequence [ 2 ]. Learn the semantic and motion characteristic information contained in the video to build the mapping between the video content and action categories so as to classify human behavior.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Method for Intelligent Analysis of Sports Training Postures

Sun

2022

Computational Intelligence and Neuroscience

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With the further research of artificial intelligence technology, motion recognition technology is widely used in posture analysis of sports training. However, the interference of light, Angle, and distance in real life makes the existing model unable to focus on the expression of human movements. Aiming at the above problems, this paper proposes a motion training attitude analysis method based on a multiscale spatiotemporal graph convolution network. Firstly, the spatiotemporal image of the skeleton is constructed, and then the convolution operation is performed on the spatiotemporal image of the skeleton. Finally, the convolution results are linearly weighted and fused to capture the characteristics of action types with different time lengths. At the same time, the algorithm increases the processing of some important information loss and increases the randomness of the data set. Experimental results show that the proposed algorithm can adapt to the behavior changes of different complexity, and the model performance and recognition accuracy are significantly improved.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Intelligent Analysis of Sports Training Postures

Sun

2022

Computational Intelligence and Neuroscience

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“…In addition to the above two feature extraction methods (HOG [43][44][45][46], SIFT [47][48][49], DTW [50,41,39,40]) for posture recognition, several feature extraction methods are widely used in posture recognition, such as Hu moment invariant (HMI) [51,52], Fourier descriptors (FD) [53,54], nonparametric weighted feature extraction (NWFE) [55,56], gray-level co-occurrence matrix (GLCM) [57,58].…”

Section: Other Feature Extraction Approachesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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“…2011-2016 2017-present Football [37]- [42] [30], [43]- [52] Basketball [53]- [59] [60]- [72] Volleyball [73]- [77] [78]-[83] Hockey [84]- [89] [90]-[99] Diving [100] [101]-[107] Tennis [108]- [113] [114]- [123] Table tennis [124]- [129] [130]-[138] Gymnastics [139]- [144] [145]-[148] Badminton [149]- [154] [155]- [164] Figure Skating [165], [166] [2], [167]- [174] Recently, researchers in the communities of computer vision and sports pay much attention to sports video analysis, including building datasets and proposing novel methodologies [2], [17]- [30]. In most existing works on sports video analysis, recognizing the actions of players in videos is crucial.…”

Section: Sportmentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

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Deep Spatial/temporal-level feature engineering for Tennis-based action recognition

Cited by 13 publications

References 8 publications

A Deep Learning Method for Intelligent Analysis of Sports Training Postures

A Deep Learning Method for Intelligent Analysis of Sports Training Postures

A Survey on Artificial Intelligence in Posture Recognition

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

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