Simone Calderara; Uri Heinemann; Andrea Prati; Rita Cucchiara; Naftali Tishby "Detecting Anomalies in Peoples Trajectories using Spectral Graph Analysis" COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 115(8), pp. 1099 -1111 , 2011 DOI: 10.1016/j.cviu.2011.03.003

Bibtex entry:

 @article{
11380_649311,
author = {Simone, Calderara and Uri, Heinemann and Andrea, Prati and Rita, Cucchiara and Naftali, Tishby},
title = {Detecting Anomalies in People’s Trajectories using Spectral Graph Analysis},
year = {2011},
journal = {COMPUTER VISION AND IMAGE UNDERSTANDING},
volume = {115(8)},
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 ormultiple cameras. In this work we synergistically combine two state-of-the-art methodologies. The rst 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 betweennodes 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 detectinganomalous trajectories, and demonstrate their application on realvideo data.},
keywords = {spectral graph theory; anomaly detection; trajectory analysis; people video surveillance},
doi = {10.1016/j.cviu.2011.03.003},
pages = {1099--1111}
}

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