Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining

Abstract

Aiming at problems of low accuracy in the recognition and difficulty in enriching the information of abnormal behavior in the social security incidents，an abnormal trajectory detection method based on enhanced density clustering and abnormal information mining was proposed.Firstly，combined with Hausdorff distance，an enhanced DTW distance aiming at the problem of sampling to describe the behavior in detail was proposed.And based on the MBR distance， some definitions to describe the geographical distribution of trajectory were proposed.Secondly，with the density-distance decision model of CFSFDP algorithm，intelligent recognition of cluster was realized by using the difference of SSVR which was proposed based on SVR.Finally，based on the analysis of distribution under the two kinds of density，more abnormal information could be mined，three kinds of abnormal trajectories would be recognized.And the simulation results on trajectory data of Shanghai and Beijing verify that the algorithm is objective and efficient.Comparing to existing method，accuracy in the clustering is promoted by 10%，and the abnormal trajectories are sorted， abnormal information is enriched.

Keywords

• SVR;density clustering;abnormal trajectory detection;pattern information mining