@misc{Fan2009c,
abstract = {In this paper we modify our unsupervised anomaly detection algorithm [1,2] and apply it to highway traffic anomaly analysis. We propose a method to identify anomalies under a prob-abilistic framework. Instead of determining anomalies based on the size of each cluster, they are determined in a prob-abilistic framework. Moreover, we present our findings on using different features when analyzing real highway vehicle trajectory data. Based on real highway traffic video data we demonstrate that the inclusion of certain features, brings us closer to identifying events that are both anomalous and abnormal (based on driving rules).},
author = {Jiang, F and Tsaftaris, SA and Wu, Y and Katsaggelos, AK},
booktitle = {CCITT Northwestern University},
title = {{Detecting anomalous trajectories from highway traffic data}},
url = {http://ccitt.northwestern.edu/documents/2009.Jiang_Tsaftaris_Wu_Katsaggelos_pub.pdf},
year = {2009}
}

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