FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309693148> ?p ?o ?g. }

• W4309693148 endingPage "9" @default.
• W4309693148 startingPage "1" @default.
• W4309693148 abstract "The traditional tennis serve training model has been deeply ingrained in the current training. For athletes, in order to master the essentials of technical movements proficiently, they must practice for a long time and repeatedly according to the guidance of coaches. Effectively correcting the image path of tennis serve can improve the level of tennis training and competition. When correcting the image path of the tennis serve, it is necessary to mark the corners of the error points according to the characteristics of rotating multidimensional characteristics of the serving action. The traditional method adopts the critical node control method to realize the extraction of limb features and complete the path correction of the serving image. The error points of the process are marked, which reduces the accuracy of the path correction. Based on the deep learning model, this paper proposes an optimization modeling method for tennis serve image path correction and builds a visual feature acquisition system for tennis serve action based on remote video monitoring. The processing method designs the edge segmentation algorithm for the collected visual images and, on this basis, marks the corner points of the error points of the serving action and realizes the optimal modeling of the path correction of the tennis serving image. In this paper, the DLT algorithm and MSDLT algorithm are compared first, and then, the DLT algorithm is compared with the algorithm in this paper. The results show that the success rate of this method is about 92%, while the success rate of DLT algorithm is only about 82%. This algorithm has obvious advantages. The method in this paper is used to correct the action shape of the tennis serving action, which has better real time and accuracy and superior performance and can accurately track the visual edge information feature points of the player during the serving process." @default.
• W4309693148 created "2022-11-29" @default.
• W4309693148 creator A5019685704 @default.
• W4309693148 creator A5023146284 @default.
• W4309693148 date "2022-11-22" @default.
• W4309693148 modified "2023-10-14" @default.
• W4309693148 title "Modeling and Simulation of Tennis Serve Image Path Correction Optimization Based on Deep Learning" @default.
• W4309693148 cites W2021979735 @default.
• W4309693148 cites W2489914180 @default.
• W4309693148 cites W2576917530 @default.
• W4309693148 cites W2586565528 @default.
• W4309693148 cites W2609386696 @default.
• W4309693148 cites W2806938108 @default.
• W4309693148 cites W2928332582 @default.
• W4309693148 cites W2982736317 @default.
• W4309693148 cites W2994172457 @default.
• W4309693148 cites W3002175584 @default.
• W4309693148 cites W3040614011 @default.
• W4309693148 cites W3088576728 @default.
• W4309693148 cites W3176157278 @default.
• W4309693148 cites W3184193523 @default.
• W4309693148 cites W3184976498 @default.
• W4309693148 cites W4221105839 @default.
• W4309693148 doi "https://doi.org/10.1155/2022/5070659" @default.
• W4309693148 hasPublicationYear "2022" @default.
• W4309693148 type Work @default.
• W4309693148 citedByCount "1" @default.
• W4309693148 countsByYear W43096931482023 @default.
• W4309693148 crossrefType "journal-article" @default.
• W4309693148 hasAuthorship W4309693148A5019685704 @default.
• W4309693148 hasAuthorship W4309693148A5023146284 @default.
• W4309693148 hasBestOaLocation W43096931481 @default.
• W4309693148 hasConcept C111919701 @default.
• W4309693148 hasConcept C115961682 @default.
• W4309693148 hasConcept C121332964 @default.
• W4309693148 hasConcept C127413603 @default.
• W4309693148 hasConcept C154945302 @default.
• W4309693148 hasConcept C199360897 @default.
• W4309693148 hasConcept C2777735758 @default.
• W4309693148 hasConcept C2780791683 @default.
• W4309693148 hasConcept C31972630 @default.
• W4309693148 hasConcept C41008148 @default.
• W4309693148 hasConcept C62520636 @default.
• W4309693148 hasConcept C62611344 @default.
• W4309693148 hasConcept C66938386 @default.
• W4309693148 hasConcept C98045186 @default.
• W4309693148 hasConceptScore W4309693148C111919701 @default.
• W4309693148 hasConceptScore W4309693148C115961682 @default.
• W4309693148 hasConceptScore W4309693148C121332964 @default.
• W4309693148 hasConceptScore W4309693148C127413603 @default.
• W4309693148 hasConceptScore W4309693148C154945302 @default.
• W4309693148 hasConceptScore W4309693148C199360897 @default.
• W4309693148 hasConceptScore W4309693148C2777735758 @default.
• W4309693148 hasConceptScore W4309693148C2780791683 @default.
• W4309693148 hasConceptScore W4309693148C31972630 @default.
• W4309693148 hasConceptScore W4309693148C41008148 @default.
• W4309693148 hasConceptScore W4309693148C62520636 @default.
• W4309693148 hasConceptScore W4309693148C62611344 @default.
• W4309693148 hasConceptScore W4309693148C66938386 @default.
• W4309693148 hasConceptScore W4309693148C98045186 @default.
• W4309693148 hasLocation W43096931481 @default.
• W4309693148 hasOpenAccess W4309693148 @default.
• W4309693148 hasPrimaryLocation W43096931481 @default.
• W4309693148 hasRelatedWork W1891287906 @default.
• W4309693148 hasRelatedWork W1969923398 @default.
• W4309693148 hasRelatedWork W2036807459 @default.
• W4309693148 hasRelatedWork W2130228941 @default.
• W4309693148 hasRelatedWork W2161229648 @default.
• W4309693148 hasRelatedWork W2166024367 @default.
• W4309693148 hasRelatedWork W2755342338 @default.
• W4309693148 hasRelatedWork W2772917594 @default.
• W4309693148 hasRelatedWork W2775347418 @default.
• W4309693148 hasRelatedWork W2993674027 @default.
• W4309693148 hasVolume "2022" @default.
• W4309693148 isParatext "false" @default.
• W4309693148 isRetracted "false" @default.
• W4309693148 workType "article" @default.