Dealing with surveillance systems, large amount of distance measures are presented in order to classify both normal and abnormal behavior. Typically, techniques based in point-to-point distances are used. However, these techniques do not take into account information about the environment, like pits or restricted areas, for instance. Using a minimal path algorithm to model the usual paths, we develop new trajectory distance measures that are able to introduce information about the scene. The results obtained show promising results. © 2013 Springer-Verlag.
CITATION STYLE
Cancela, B., Ortega, M., Fernández, A., & Penedo, M. G. (2013). Trajectory similarity measures using minimal paths. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8156 LNCS, pp. 400–409). https://doi.org/10.1007/978-3-642-41181-6_41
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