PurposeSpectators have a significant impact on match performances in soccer, but to what extent crowd support contributes to the technical and physical performances remains unclear. This study aimed to (1) investigate the differences in terms of technical and physical performances with and without spectators; and (2) identify the key factors differentiating between win and loss when playing with and without the presence of an audience.MethodsOur study examined 794 performance records from 397 matches during the 2019–2020 seasons in the Chinese Soccer Super League. The least absolute shrinkage and selection operator (LASSO)-logistic regression was utilized to select significant predictors. Using an independent t-test and the Mann–Whitney non-parametric test explores the difference between matches with and without spectators. Key factors between win and loss were explored using univariate and multivariate logistic regression analyses.ResultsOur study found that cross (p < 0.01, ES = −0.24), shots (p < 0.001, ES = −0.25), and shot accuracy (p < 0.05, ES = −0.18) displayed decreasing trends whereas sprint distances (p < 0.05, ES = 0.16) presented an increasing trend without spectators comparing with the crowd support. Moreover, the above three technical variables were the main factors differentiating between wins and losses. Similarly, team and opponent quality remained important potential factors affecting the match outcome.ConclusionMatch outcome or team performance is determined by a myriad of factors, but there are clear differences in technical and physical performances between matches with and without the presence of an audience. Similarly, our study provides a better explanation for the impact of crowd support on match performances whereby coaches can deploy players and adjust match strategies for ultimate success.
In traditional convolutional networks, due to the lack of adequate information protection of traditional image fusion technology and the incomplete removal of redundant noise, the useful information of the fusion image is missing and the recognition success rate is low. In this paper, through the research of deep learning-based image fusion methods and traditional target recognition and SVM neural network, an image fusion processing recognition method based on infrared and visible light is designed. The coding network of this image fusion method consists of convolutional layers, fusion layers and dense blocks. The output of each layer needs to be connected to the next few layers by using a densely connected neural network, so as to obtain more useful features from the source image and fuse the data of the two images better. It is verified by simulation that the fused image has sound visual effects, and its edges and details have been completely preserved. Thus, the target object has strong recognizability compared with the surrounding environment. Research shows that this method will help to more accurately interpret target information in complex environments and achieve more effective results in target recognition.
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