A framework for automatic sports video annotation with anomaly detection and transfer learning
A framework for automatic sports video annotation with anomaly detection and transfer learning
复制标题
具有异常检测和迁移学习的自动体育视频注释框架
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
W. Christmas
中科院分区：
文献类型：
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作者：
Ted Campos;Aftab Khan;F. Yan;N. Davar;David Windridge;J. Kittler;W. Christmas
本文介绍了一种视频自动标注系统，并举例说明了该系统在网球比赛中的应用。在贝叶斯推理框架中提出了一种统一的仪器。这得到了认知记忆架构的支持，该架构允许系统在最低认知级别存储原始视频数据及其语义注释，并随着抽象级别的增加而存储，直至确定游戏的分数。该系统中还嵌入了一套机制，用于检测输入数据中域的变化引起的异常。一旦检测到异常，就触发迁移学习方法以使知识适应新的领域，例如新的运动模式。我们还提出了一个通用的规则归纳框架，这在自适应标注系统的上下文中是至关重要的。
This paper describes a system that can automatically annotate videos and illustrates its application to tennis games. A unified apparatus is proposed, cast in a Bayesian reasoning framework. This is supported by a cognitive memory architecture that allows the system to store raw video data at the lowest cognitive level and its semantic annotation with increasing levels of abstraction up to determining the score of a game. Also embedded in the system is a set of mechanisms to detect anomalies caused by a change of domain in the input data. Once an anomaly is detected, transfer learning methods are triggered to adapt the knowledge to new domains, such as new sport modalities. We also present a generic framework for rule induction that is crucial in the context of an adaptive annotation system.
DOI：
10.1007/978-3-642-24034-8_9
发表时间：
2012
期刊：
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影响因子：
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作者：
Almajai I
通讯作者：
Almajai I