Unsupervised Modeling of Object Tracks for Fast Anomaly Detection

A key goal of far-field activity analysis is to learn the usual pattern of activity in a scene and to detect statistically anomalous behavior. We propose a method for unsupervised, multi-attribute learning of a model of moving object tracks that enables fast reasoning about new tracks, both partial and complete. We group object tracks using spectral clustering and estimate the spectral embedding efficiently from a sample of tracks using the Nystrom approximation. Clusters are modeled as Gaussians in the embedding space and new tracks are projected into the embedding space and matched with the cluster models to detect anomalies. We show results on a week of data from a busy urban scene.