Given the well-estimated usual event model and an un- seen test sequence, we first slice the test sequence into fixed length segments with overlapping. This is done by mov- ing a sliding window. The choice of the sliding window size corresponds to the minimum duration constraint in the HMM framework. Given the usual event model, the likeli- hood of each segment is then calculated. The segment with the lowest likelihood value is identified as an outlier (Figure 2, step 1). The outlier is expected to represent one specific unusual event and could be used to train an unusual event model. However, one single outlier is obviously insufficient to give a good.
