Given the new unusual and usual event models, both adapted from the general usual event model, the HMM topology is changed with one more state. Hence the cur- rent HMM has 2 states, one representing the usual events and one representing the first detected unusual event. The Viterbi algorithm is then used to find the best possible state sequence which could have emitted the observation sequence, according to the maximum likelihood (ML) cri- terion (Figure 2, step 3). Transition points, which define new segments, are detected using the current HMM topol- ogy and parameters. A new outlier is now identified by sorting the likelihood of all segments given the usual event model.
