Detecting abnormal events such as crash is a practical problem that is important to Intelligent Transportation System. By taking advantage of the data recorded by the remote sensors which are deployed along the road, we can perform data mining techniques to see whether there are abnormal events happening on the road. This paper aims at proposing an abnormal-events-detecting method based on the traffic data, which first utilizes outlier detection to generate a fuzzy result set from source data, and then through the time series mining techniques to filter that to obtain an accurate experimental one. Experiment with real-world data shows that our method works satisfactorily in detecting abnormalities such crash, stall and hazard on the road.
Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes
