Video based basketball shooting prediction and pose suggestion system

Chen, Chien‐Chang; Chang, Chen; Lin, Cheng-Shian; Chen, Chien‐Hua; Chen, I. Cheng

doi:10.1007/s11042-023-14490-2

Cited by 3 publications

(3 citation statements)

References 76 publications

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“…The shading technique used in the figure is the fill between function, which effectively illustrates the attention weights of each curve (He and Ye, 2022 ). By assigning darker colors to areas with higher attention weights and lighter colors to areas with lower attention weights, the figure visually represents the essence of the attention mechanism (Chen et al, 2023 ). This mechanism aims to improve sequence learning by assigning varying weights to different parts of the input sequence (Saiki et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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IntroductionWith the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.MethodsThe proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.ResultsExperimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.DiscussionIn conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

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Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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“…In recent years, many studies have been conducted that analyzed the performance of individual players in sports domains [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. While there are many studies that focus on motion analysis, most require athletes to wear sensors to acquire their posture data; however, this method easily introduces noise due to friction during motions [ 1 , 2 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…While there are many studies that focus on motion analysis, most require athletes to wear sensors to acquire their posture data; however, this method easily introduces noise due to friction during motions [ 1 , 2 , 8 ]. Therefore, the video-based contactless approach, which aims to acquire whole posture data using a simple camera without placing sensors on body parts, has made it easier and more convenient for scholars to analyze a variety of postures in many exciting sports domains [ 3 , 4 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network

Chen,

Lin,

Chen

et al. 2023

J. Imaging

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Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience. The aim of our work is to use a graph-matching network to analyze the similarity in rowers’ rowing posture and further pair rowers to improve the performance of their rowing team. This study proposed a novel video-based performance analysis system to analyze paired rowers using a graph-matching network. The proposed system first detected human joint points, as acquired from the OpenPose system, and then the graph embedding model and graph-matching network model were applied to analyze similarities in rowing postures between paired rowers. When analyzing the postures of the paired rowers, the proposed system detected the same starting point of their rowing postures to achieve more accurate pairing results. Finally, variations in the similarities were displayed using the proposed time-period similarity processing. The experimental results show that the proposed time-period similarity processing of the 2D graph-embedding model (GEM) had the best pairing results.

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Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

Duan

Liu³

et al. 2023

Applied Sciences

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Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.

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Video based basketball shooting prediction and pose suggestion system

Cited by 3 publications

References 76 publications

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network

Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

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