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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification

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IEEE ACCESS 2025年 13卷 25409-25418页

作者： Yang, Weichao Luoyang Inst Sci & Technol Dept Phys Educ Luoyang 471023 Peoples R China

To address the issues of accuracy and generalization in action recognition within complex tennis training scenarios, this study proposes an Adaptive Semantic-Enhanced convolutional neural network (ASE-CNN) model. The model optimizes multimodal data integration and complex action classification performance, enabling precise analysis of key action features in tennis training. Experimental results demonstrate that ASE-CNN excels in multimodal feature extraction, achieving a Receiver Operating Characteristic-Area Under Curve (ROC-AUC) value of 0.85-0.95 and a Feature Distribution Uniformity of 0.75-0.85, validating its accuracy and stability in feature classification. The fused weighted Precision-Recall Area Under Curve (PR-AUC) value ranges from 0.80 to 0.92, highlighting its capability for synergistic optimization of multimodal data. For key action recognition, the classification accuracy reaches up to 0.98, with Euclidean distances between key actions and ideal action templates as low as 0.05-0.15, demonstrating exceptional fine-detail discrimination. Compared to state-of-the-art models, ASE-CNN exhibits significant advantages in per-frame processing time and resource utilization efficiency, offering potential for efficient real-time feedback in resource-constrained environments. This study provides scientific evidence and technical support for optimizing actions and improving strategies in professional training.

关键词： Training Sports Data models convolutional neural networks Computational modeling Semantics Adaptation models Feature extraction Analytical models Accuracy Tennis action recognition convolutional neural network architecture multimodal data feature extraction ASE-CNN

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A combination of feature extraction methods and deep learning for brain tumour classification

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IET IMAGE PROCESSING 2022年第2期16卷 416-441页

作者： Siar, Masoumeh Teshnehlab, Mohammad Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran KN Toosi Univ Technol Fac Elect & Comp Engn Tehran Iran

This paper presents a method for categorizing tumour disease from magnetic resonance imaging images using a convolutional neural network. The proposed technique consists of three major phases, including feature extraction, feature selection, and combination. The authors considered the classification method using convolutional neural network without any pre-processing on input images as the original method. The original method is then improved in some sequential phases when convolutional neural network uses features generated by feature extraction methods. Many popular feature extraction methods in generating the input features of the network are examined. After examining the results, a set of feature extraction schemes with appropriate performance are selected for the next phase. Also, the authors assigned weight factors to each of the selected methods, according to their accuracy. By assigning these weight factors to the methods, the network's accuracy increased to an acceptable level compared to the first and second observations. An accuracy above 99.76% was achieved, which is a 2% improvement using these feature extraction methods compared to the original method. The authors also added a third class named 'I do not know' to increase the tumour detection problem's reliability in the last phase. The authors have successfully introduced a convolutional neural network architecture with high accuracy and reliability.

关键词： tumour detection problem reliability diseases classification method convolutional neural nets deep learning feature extraction schemes reliability weight factors convolutional neural network architecture feature extraction deep learning (artificial intelligence) magnetic resonance imaging images tumour disease brain biomedical MRI medical image processing brain tumour classification image classification tumours feature selection

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A lightweight deep learning model for cattle face recognition

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 195卷 1页

作者： Li, Zheng Lei, Xuemei Liu, Shuang Inner Mongolia Univ Sch Elect Informat Engn Hohhot 010020 Inner Mongolia Peoples R China

At present, there have been some researches on deep neural networks used in the field of biometrics to solve the problem of cattle identity authentication, particularly facial recognition under non-invasive methods. However, due to the large scale of deep neural network models, it is difficult to implement directly in resource-constrained embedded systems. Therefore, a lightweight neural network is designed in this paper that can be deployed in embedded systems that requires a small amount of weight representations and low-cost operators. The lightweight neural network designed in this paper contains six convolutional layers, which the convolution with a step size of two is used to achieve pooling, the Batch Normalization algorithm is used to normalize the neural network input, the dropout layer is used after the activation function Relu, and global average pooling is used to replace the fully connected layer after the sixth convolutional layer. The final parameter is 0.17 M, the model size is 2.01 MB, and the MFLOPs is 9.17. We have collected 10,239 cattle face images (103 subjects) from actual cattle farms to construct a dataset. The experimental results show that the accuracy of our proposed model is 98.37%, the macro average is 98%, and the Grad-CAM algorithm is used to verify the adequacy of features extracted by this network and avoid extracting noise features. The model we proposed is transplanted to the Raspberry Pi after training on the PC to test the running time, the model we proposed achieves a minimum detection time of 300 ms per picture. Our experiment results show that the lightweight neural network we proposed can achieve high recognition accuracy for cattle face recognition and significantly reduce the computational cost.

关键词： convolutional neural network architecture Accuracy Model size MFLOPs Detection time

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Ear verification under uncontrolled conditions with convolutional neural networks

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IET BIOMETRICS 2018年第3期7卷 185-198页

作者： Zhang, Yi Mu, Zhichun Yuan, Li Yu, Chen Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Xian Musheng Elect Technol Co Ltd Xian Shaanxi Peoples R China

The capabilities of biometric systems have recently made extraordinary leaps by the emergence of deep learning. However, due to the lack of enough training data, the applications of the deep neural network in the ear recognition filed have run into the bottleneck. Moreover, the effect of fine-tuning from some pre-trained models is far less than expected due to the diversity among different tasks. Therefore, the authors propose a large-scale ear database and explore the robust convolutional neural network (CNN) architecture for the ear feature representation. The images in this USTB-Helloear database were taken under uncontrolled conditions with illumination, pose variation and different level of ear occlusions. Then they fine-tuned and modified some deep models on the proposed database through the ear verification experiments. First, they replaced the last pooling layers by spatial pyramid pooling layers to fit arbitrary data size and obtain multi-level features. In the training phase, the CNNs were trained both under the supervision of the softmax loss and centre loss to obtain more compact and discriminative features to identify unseen ears. Finally, three CNNs with different scales of ear images were assembled as the multi-scale ear representations for ear verification. The experimental results demonstrate the effectiveness of the proposed modified CNN deep model.

关键词： feature extraction biometrics (access control) image representation ear image recognition convolution learning (artificial intelligence) feedforward neural nets biometric systems deep learning deep neural network ear recognition large-scale ear database ear feature representation USTB-Helloear database ear occlusions spatial pyramid pooling layers arbitrary data size multilevel features softmax loss discriminative features unseen ears multiscale ear representations convolutional neural network architecture ear verification CNN deep model

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