Using Inactivity to Detect Unusual behavior

We present a novel method for detecting unusual modes of behavior in video surveillance data, suitable for supporting home-based care of elderly patients. Our approach is based on detecting unusual patterns of inactivity. We first learn a spatial map of normal inactivity for an observed scene, expressed as a two-dimensional mixture of Gaussians. The map components are used to construct a Hidden Markov Model representing normal patterns of behavior. A threshold model is also inferred, and unusual behavior detected by comparing the model likelihoods. Our learning procedures are unsupervised, and yield a highly transparent model of scene activity. We present an evaluation of our approach, and show that it is effective in detecting unusual behavior across a range of parameter settings.