A Deep Learning Algorithm for Special Action Recognition of Football

Wang, Sheng

doi:10.1155/2022/6315648

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…HAR has been applied in various domains, including security [3][4][5], healthcare [6][7][8], and sports [9][10][11]. Vision-based HAR systems have been employed to support quantitative evaluation and judgment in various sports [12][13][14][15][16][17][18]. However, few studies have reported the application of action recognition technology in martial arts disciplines, such as Taekwondo [1,2].…”

Section: Introductionmentioning

confidence: 99%

Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets

Luo,

Kim,

Park

et al. 2023

Sensors

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Issues of fairness and consistency in Taekwondo poomsae evaluation have often occurred due to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network–based action recognition model for an objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variations in the viewpoints by reducing the discrepancy between the training and test images. It uses 3D skeletons of poomsae unit actions collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from desired viewpoints. The 2D skeletons obtained from diverse viewpoints form the training dataset, on which the model is trained to ensure consistent recognition performance regardless of the viewpoint. The performance of the model was evaluated against various test datasets, including projected 2D skeletons and RGB images captured from diverse viewpoints. Comparison of the performance of the proposed model with those of previously reported action recognition models demonstrated the superiority of the proposed model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.

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Section: Introductionmentioning

confidence: 99%

Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets

Luo,

Kim,

Park

et al. 2023

Sensors

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Face recognition in online soccer streaming for piracy detection

A. Correia,

Pontes,

Henrique Brito

2024

Multimed Tools Appl

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This paper presents a face recognition system that is part of a global solution for online soccer piracy detection. The overall solution uses several building blocks to detect illegal sharing of live soccer broadcasts. This paper presents one of the building blocks, a face recognition system, that recognizes the faces of players that participate in the soccer match that the overall solution is trying to identify. The face recognition system detects faces in the broadcast image frames and tries to match them to a database faces of players from the clubs that participate in the game. The proposed face recognition system uses Retinaface and OpenCV to detect faces, applies Deep Learning networks Facenet128 and FaceNet512 to extract features from the detected faces, computes the cosine distance between features to evaluate the dissimilarity between faces, and compares the distance to a predefined threshold. This approach aims to maximize Precision and True Positive Rate while ensuring a False Positive Rate equal to zero, even at the cost of a lower Recall and Accuracy, and also provides results in as close to real-time as possible. The experiments show that the proposed face recognition system is able to achieve a True Positive Rate of 38.4% while ensuring a False Positive Rate of 0, which is an important aspect for the overall solution. The system is able to analyze an average of 20 frames per second. The results show the potential of this approach to identify and combat illegal broadcasts of sporting events, offering a robust approach to address the escalating issue of unauthorized audiovisual content sharing.

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A Review of Deep Learning Architectures for Automated Video Analysis in Football Events

2024

2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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A Deep Learning Algorithm for Special Action Recognition of Football

Cited by 4 publications

References 29 publications

Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets

Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets

Face recognition in online soccer streaming for piracy detection

A Review of Deep Learning Architectures for Automated Video Analysis in Football Events

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