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      An Action Recognition Algorithm for Sprinters Using Machine Learning

      1 , 2
      Mobile Information Systems
      Hindawi Limited

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          Abstract

          The advancements in modern science and technology have greatly promoted the progress of sports science. Advanced technological methods have been widely used in sports training, which have not only improved the scientific level of training but also promoted the continuous growth of sports technology and competition results. With the development of sports science and the gradual deepening of sport practices, the use of scientific training methods and monitoring approaches has improved the effect of sports training and athletes’ performance. This paper takes sprint as the research problem and constructs the image of sprinter’s action recognition based on machine learning. In view of the shortcomings of traditional dual-stream convolutional neural network for processing long-term video information, the time-segmented dual-stream network, based on sparse sampling, is used to better express the characteristics of long-term motion. First, the continuous video frame data is divided into multiple segments, and a short sequence of data containing user actions is formed by randomly sampling each segment of the video frame sequence. Next, it is applied to the dual-stream network for feature extraction. The optical flow image extraction involved in the dual-stream network is implemented by the system using the Lucas–Kanade algorithm. The system in this paper has been tested in actual scenarios, and the results show that the system design meets the expected requirements of the sprinters.

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          TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification

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            The Training and Development of Elite Sprint Performance: an Integration of Scientific and Best Practice Literature

            Despite a voluminous body of research devoted to sprint training, our understanding of the training process leading to a world-class sprint performance is limited. The objective of this review is to integrate scientific and best practice literature regarding the training and development of elite sprint performance. Sprint performance is heavily dependent upon genetic traits, and the annual within-athlete performance differences are lower than the typical variation, the smallest worthwhile change, and the influence of external conditions such as wind, monitoring methodologies, etc. Still, key underlying determinants (e.g., power, technique, and sprint-specific endurance) are trainable. In this review, we describe how well-known training principles (progression, specificity, variation/periodization, and individualization) and varying training methods (e.g., sprinting/running, technical training, strength/power, plyometric training) are used in a sprint training context. Indeed, there is a considerable gap between science and best practice in how training principles and methods are applied. While the vast majority of sprint-related studies are performed on young team sport athletes and focus on brief sprints with maximal intensity and short recoveries, elite sprinters perform sprinting/running over a broad range of distances and with varying intensity and recovery periods. Within best practice, there is a stronger link between choice of training component (i.e., modality, duration, intensity, recovery, session rate) and the intended purpose of the training session compared with the “one-size-fits-all” approach in scientific literature. This review provides a point of departure for scientists and practitioners regarding the training and development of elite sprint performance and can serve as a position statement for outlining state-of-the-art sprint training recommendations and for generation of new hypotheses to be tested in future research.
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              An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

              In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.
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                Author and article information

                Contributors
                Journal
                Mobile Information Systems
                Mobile Information Systems
                Hindawi Limited
                1875-905X
                1574-017X
                May 19 2021
                May 19 2021
                : 2021
                : 1-10
                Affiliations
                [1 ]Institute of Science, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
                [2 ]Physical Education Department, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China
                Article
                10.1155/2021/9919992
                81aa1400-fda4-4a90-a9ab-be217320a24a
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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