Detecting anomalies in people's trajectories using spectral graph analysis

  • Authors:

  • Simone Calderara;Uri Heinemann;Andrea Prati;Rita Cucchiara;Naftali Tishby

  • Affiliations:

  • Department of Information Engineering, University of Modena and Reggio Emilia, Via Vignolese, 905/b - 41122 Modena, Italy;School of Engineering and Computer Science, The Hebrew University, Jerusalem 91904, Israel;Department of Engineering Science and Methods, University of Modena and Reggio Emilia, Via Amendola, 2, 42122 Reggio Emilia, Italy;Department of Information Engineering, University of Modena and Reggio Emilia, Via Vignolese, 905/b - 41122 Modena, Italy;School of Engineering and Computer Science, The Hebrew University, Jerusalem 91904, Israel

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

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Abstract

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 the low eigenvalues of the graph Laplacian matrix. Using this technique, we develop robust invariant distance measures for detecting anomalous trajectories, and demonstrate their application on real video data.