首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model
【24h】

Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model

机译:识别稀有和微妙的行为:弱监督的联合主题模型

获取原文
获取原文并翻译 | 示例

摘要

One of the most interesting and desired capabilities for automated video behavior analysis is the identification of rarely occurring and subtle behaviors. This is of practical value because dangerous or illegal activities often have few or possibly only one prior example to learn from and are often subtle. Rare and subtle behavior learning is challenging for two reasons: 1) Contemporary modeling approaches require more data and supervision than may be available and 2) the most interesting and potentially critical rare behaviors are often visually subtleȁ4;occurring among more obvious typical behaviors or being defined by only small spatio-temporal deviations from typical behaviors. In this paper, we introduce a novel weakly supervised joint topic model which addresses these issues. Specifically, we introduce a multiclass topic model with partially shared latent structure and associated learning and inference algorithms. These contributions will permit modeling of behaviors from as few as one example, even without localization by the user and when occurring in clutter, and subsequent classification and localization of such behaviors online and in real time. We extensively validate our approach on two standard public-space data sets, where it clearly outperforms a batch of contemporary alternatives.
机译:用于自动视频行为分析的最有趣和最需要的功能之一是识别很少发生的细微行为。这具有实用价值,因为危险或非法活动通常很少或可能只有一个先例可以学习,并且往往很微妙。稀有和微妙的行为学习具有挑战性,原因有二:1)当代建模方法需要更多的数据和监控,而可能无法提供; 2)最有趣和潜在关键的稀有行为通常在视觉上微妙ȁ4;发生在更明显的典型行为中或已被定义与典型行为之间只有很小的时空偏差。在本文中,我们介绍了一种解决这些问题的新型弱监督联合主题模型。具体来说,我们介绍了一个多类主题模型,该模型具有部分共享的潜在结构以及相关的学习和推理算法。这些贡献将允许仅从一个示例就对行为进行建模,即使没有用户本地化并且在混乱中发生时,也可以在线实时地对此类行为进行后续分类和本地化。我们在两个标准的公共空间数据集上对我们的方法进行了广泛的验证,该数据集明显优于一系列当代替代方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号