Research
Publications and applied research outputs spanning action spotting, tactical classification, and real-time sports AI systems.
Featured Research
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Journal Article
Real-time Analysis of Indoor Sports Game Situations through Deep Learning-based Classification
96.1% macro F1 on handball and 92.5% on basketball with sub-4 ms inference and 34.04 ms end-to-end pipeline latency.
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Proceedings Paper
From Raw Feeds to Curated Clips: A Modular AI Framework for Sports Highlight Production
Reduced post-event highlight processing time by 87.5% while maintaining 90% event-selection accuracy.
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All Research
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Real-time Analysis of Indoor Sports Game Situations through Deep Learning-based Classification96.1% macro F1 on handball and 92.5% on basketball with sub-4 ms inference and 34.04 ms end-to-end pipeline latency.
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From Raw Feeds to Curated Clips: A Modular AI Framework for Sports Highlight ProductionReduced post-event highlight processing time by 87.5% while maintaining 90% event-selection accuracy.
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Play by Play, a dataset of handball and basketball game situations in a standardized spaceReleased a synthetic labeled dataset with 365,383 sports frames covering seven standardized game situations.
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Large Language Model Based Chatbot for Database Interaction through Natural LanguageDemonstrated practical NL-to-SQL interaction quality across four LLMs using human evaluation metrics.
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OSL-ActionSpotting, A unified library for action spotting in sports videosUnified fragmented action-spotting methods into one reusable library pipeline for faster experimentation.
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Beyond the Premier, Assessing action spotting transfer capability across diverse domainsBroadcast editing style appears as the dominant factor affecting cross-domain action-spotting transfer.
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Real-time classification of handball game situations98.6% classification accuracy with 4 ms inference and 34.04 ms end-to-end pipeline latency.
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