Computer Vision and Pattern Recognition (CVPR)
Computer Vision and Pattern Recognition (CVPR)
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DOI：
10.1109/cvpr.2009.5206636
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
D. Damen;David C. Hogg
中科院分区：
文献类型：
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作者：
D. Damen;David C. Hogg
从视频中的事件的本地化分析中固有的模糊性可以通过利用事件之间的约束和检查仅可行的全局解释来解决。我们展示了如何联合识别和链接事件可以制定为标签的贝叶斯网络。该框架可以扩展到多个链接层，将解释表达为组合层次结构。最好的全局解释是一组可行解释上的最大后验（MAP）解。使用可逆跳马尔可夫链蒙特卡罗（RJMCMC）的搜索空间进行采样。我们提出了一套通用的移动类型，可扩展到多层的链接，并使用模拟退火找到MAP的解决方案，所有的意见。我们提供了一个具有挑战性的两层联动问题的实验结果，展示了能够识别和链接下降，并挑选事件的自行车在机架上超过五天。
The ambiguity inherent in a localized analysis of events from video can be resolved by exploiting constraints between events and examining only feasible global explanations. We show how jointly recognizing and linking events can be formulated as labeling of a Bayesian network. The framework can be extended to multiple linking layers, expressing explanations as compositional hierarchies. The best global explanation is the maximum a posteriori (MAP) solution over a set of feasible explanations. The search space is sampled using reversible jump Markov chain Monte Carlo (RJMCMC). We propose a set of general move types that is extensible to multiple layers of linkage, and use simulated annealing to find the MAP solution given all observations. We provide experimental results for a challenging two-layer linkage problem, demonstrating the ability to recognise and link drop and pick events of bicycles in a rack over five days.