In this paper, we propose a space Markov Random Field (MRF) model to detect abnormal activities in crowded
scenes. The nodes of MRF graph consist of monitors evenly spread on the image, and neighboring nodes in space are
associated with links. The normal patterns of activity at each node are learnt by constructing a Gaussian Mixture Model
(GMM) upon optical flow locally, while correlation between adjacent nodes is represented by building a single Gaussian
model upon inner product of histogram vectors of optical flow observed from a region centered at each node respectively.
For any optical flow patterns detected in test video clips, we use the learnt model and MRF graph to calculate an energy
value for each local node, and determine whether the behavior pattern of the node is normal or abnormal by comparing
the value with a threshold. Further, we apply a method similar to updating of GMM for background subtraction to
incrementally update the current model to adapt for visual context changes over a long period of time. Experiments on
the published UCSD anomaly datasets Ped1 and Ped2 show the effectiveness of our method.
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