Abstract
Trajectory clustering has been used to very effectively in the detection of anomalous behavior in video sequences. A key point in trajectory clustering is how to measure the (dis)similarity between two trajectories. This paper deals with a new dissimilarity measure for trajectory clustering, giving the same importance to differences and similarities between the trajectories. Experimental results in the task of anomalous detection via hierarchical clustering shows the validity of the proposed approach.
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Espinosa-Isidrón, D.L., García-Reyes, E.B. (2010). A New Dissimilarity Measure for Trajectories with Applications in Anomaly Detection. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_29
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DOI: https://doi.org/10.1007/978-3-642-16687-7_29
Publisher Name: Springer, Berlin, Heidelberg
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