Automatic Pose Recognition in Basketball Videos Using Entropy, Mean and Standard Deviation
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Abstract
Most existing models for automatic action recognition in basketball videos lack privacy-friendly analytics, versatility and explainability. So, coaches, players and analysts often invest substantial resources by relying heavily on visual appearance, ball tracking and court context. Unfortunately, this method can be resource-intensive and potentially susceptible to unforeseeable intrusions. This study proposes an entropy-based analytical model for automatic recognition of key basketball actions, designed to optimize the video review process to address the above limitations. The model is implemented with Python programming language to analyze entropy arrays, the mean and standard deviation values derived from 22 basketball game videos. Evaluation suggests that the model flagged basketball_Video2, Video3 and Video9 as containing key moments deserving closer inspection. This has successfully reduced the input datasets to just three critical videos (with mean and standard deviation pairs of 1.96 & 0.33, 2.05 & 0.31, and 1.94 & 0.20) that warrant detailed examination. This targeted filtering significantly improves review efficiency by conserving time and resources and effectively eliminated 19 videos deemed redundant or of lower priority. The approach demonstrates high precision in identifying impactful gameplay moments and addresses a long-standing challenge with workload reduction in basketball analytics without sacrificing review accuracy. Consequently, this method not only supports privacy-conscious analytics but also provides coaches, players and sports analysts with a more focused, resource-efficient framework they can adopt for performance evaluation and strategic decision-making in basketball.
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