Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates
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DOI：
10.1109/cvpr.2009.5206569
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Jaechul Kim;K. Grauman
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文献类型：
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作者：
Jaechul Kim;K. Grauman
提出了一种时空马尔可夫随机场(MRF)模型来检测视频中的异常活动。MRF图中的节点对应于视频帧中的局部区域的网格，并且空间和时间上的相邻节点都与链接相关联。为了了解每个局部节点的正常活动模式，我们使用混合的概率主成分分析器来捕获其典型光流的分布。对于在输入视频片段中检测到的任何新的光流模式，我们使用学习的模型和马尔可夫随机场图来计算每个局部节点的正常度的最大后验估计。进一步，我们展示了如何随着新的视频观测的流入而增量地更新当前的模型参数，以便模型能够有效地适应长时间内的视觉上下文变化。在监控视频上的实验结果表明，我们的时空MRF模型在局部和全局意义上都能稳健地检测到异常活动：它不仅能够准确地定位拥挤视频中的原子异常活动，而且还捕捉到了局部活动之间不规则交互所导致的全局级别的异常。
We propose a space-time Markov random field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video frames, and neighboring nodes in both space and time are associated with links. To learn normal patterns of activity at each local node, we capture the distribution of its typical optical flow with a mixture of probabilistic principal component analyzers. For any new optical flow patterns detected in incoming video clips, we use the learned model and MRF graph to compute a maximum a posteriori estimate of the degree of normality at each local node. Further, we show how to incrementally update the current model parameters as new video observations stream in, so that the model can efficiently adapt to visual context changes over a long period of time. Experimental results on surveillance videos show that our space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.