基于动态贝叶斯网络的语义体育视频分析通用框架

Fei Wang, Yu-Fei Ma, HongJiang Zhang, Jintao Li
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引用次数: 45

摘要

体育视频中语义事件的自动检测是一项具有挑战性的任务。本文提出了一种基于动态贝叶斯网络(dbn)的多模态多层统计推理框架,用于语义体育视频分析。在此框架下,构造了阶乘层次隐马尔可夫模型(FHHMM)、耦合层次隐马尔可夫模型(CHHMM)和产品层次隐马尔可夫模型(PHHMM) 3个实例并进行了比较。以层次隐马尔可夫模型(HHMM)为基准,以足球视频中的比赛中断检测为测试平台。实验结果表明,PHHMM不仅有效地模拟了不同模态之间的动态相互作用,而且充分利用了多层结构中的上下文约束,具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generic Framework for Semantic Sports Video Analysis Using Dynamic Bayesian Networks
Automatic detection of semantic events in sport videos is a challenging task. In this paper, we propose a multimodal multilayer statistical inference framework for semantic sports video analysis using Dynamic Bayesian Networks (DBNs). Based on this framework, three instances including factorial hierarchical hidden Markov model (FHHMM), coupled hierarchical hidden Markov model (CHHMM), and product hierarchical hidden Markov model (PHHMM), are constructed and compared. Play-break detection in soccer videos is used as a testbed with hierarchical hidden Markov model (HHMM) as a baseline. Experimental results indicate the superior capability of the PHHMM, because it not only effectively models dynamic interactions between different modalities, but also sufficiently utilizes context constraints in multilayer structures.
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