Automatic suspicious behavior detection from a small bootstrap set

Abstract

We propose and evaluate a new method for automatic identification of suspicious behavior in video surveillance data. It partitions the bootstrap set into clusters then assigns new observation sequences to clusters based on statistical tests of HMM log likelihood scores. In an evaluation on a real-world testbed video surveillance data set, the method achieves a false alarm rate of 7.4% at a 100% hit rate. It 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.