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Video surveillance is becoming the technology of choice for monitoring crowded areas for security threats. While video provides ample information for human inspectors, there is a great need for robust automated techniques that can efficiently detect anomalous behavior in streaming video from single or multiple cameras. In this work we synergistically combine two state-of-the-art methodologies. The first is the ability to track and label single person trajectories in a crowded area using multiple video cameras, and the second is a new class of novelty detection algorithms based on spectral analysis of graphs. By representing the trajectories as sequences of transitions between nodes in a graph, shared individual trajectories capture only a small subspace of the possible trajectories on the graph. This subspace is characterized by large connected components of the graph, which are spanned by the eigenvectors with …