Detecting Anomalous Trajectories from Highway Traffic Data
نویسندگان
چکیده
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 probabilistic framework. Instead of determining anomalies based on the size of each cluster, they are determined in a probabilistic 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).
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