Video Behaviour Mining Using a Dynamic Topic Model
Video Behaviour Mining Using a Dynamic Topic Model
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DOI：
10.1007/s11263-011-0510-7
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发表时间：
2012-07-01
影响因子：
19.5
通讯作者：
Xiang, Tao
中科院分区：
文献类型：
--
作者：
Hospedales, Timothy;Gong, Shaogang;Xiang, Tao
本文讨论了公共空间视频数据的全自动挖掘问题，这是当代商业和安全考虑下非常理想的能力。这项任务是特别具有挑战性的，由于对象行为的复杂性，分析的困难下，在公共空间视频中常见的视觉遮挡和模糊性，并在实时这样做的计算挑战。我们解决这些问题，通过引入一个新的动态主题模型，称为马尔可夫聚类主题模型（MCTM）。MCTM建立在现有的动态贝叶斯网络模型和贝叶斯主题模型的基础上，克服了它们在敏感性、鲁棒性和效率上的不足。具体来说，我们的模型配置文件复杂的动态场景，通过强大的聚类视觉事件到活动和这些活动到全球行为与时间动态。吉布斯采样器推导出离线学习与未标记的训练数据和一个新的近似在线贝叶斯推理制定，使动态场景的理解和行为挖掘新的视频数据在线实时。通过对四个复杂而拥挤的公共场景的动态场景模型的无监督学习，以及对每个场景中的行为和显著事件的成功挖掘，证明了该模型的优势。
This paper addresses the problem of fully automated mining of public space video data, a highly desirable capability under contemporary commercial and security considerations. This task is especially challenging due to the complexity of the object behaviors to be profiled, the difficulty of analysis under the visual occlusions and ambiguities common in public space video, and the computational challenge of doing so in real-time. We address these issues by introducing a new dynamic topic model, termed a Markov Clustering Topic Model (MCTM). The MCTM builds on existing dynamic Bayesian network models and Bayesian topic models, and overcomes their drawbacks on sensitivity, robustness and efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into global behaviours with temporal dynamics. A Gibbs sampler is derived for offline learning with unlabeled training data and a new approximation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models for four complex and crowded public scenes, and successful mining of behaviors and detection of salient events in each.