AUTOMATIC SUSPICIOUS BEHAVIOR DETECTION FROM A SMALL BOOTSTRAP SET
Kan Ouivirach, Shashi Gharti, Matthew N. Dailey · 2012
We propose and evaluate a new method for automatic identification of suspicious behavior in video surveillance data. The approach works by constructing scene-specific statistical models explaining the behaviors occurring in a small bootstrap data set. It partitions the bootstrap set into clusters then assigns new observation sequences to clusters based on statistical tests of HMM log likelihood scores. Cluster-specific likelihood thresholds are learned rather than set arbitrarily. In an evaluation on a real-world testbed video surveillance data set, the method proves extremely effective, with a false alarm rate of 7.4% at a 100% hit rate. The method is thus a practical and effective solution to the problem of inducing scene-specific statistical models useful for bringing suspicious behavior to the attention of human security personnel.