Clustering-based abnormal event detection: Experimental comparison for similarity measures’ efficiency

Abstract

The detection of abnormal events is a major challenge in video surveillance systems. In most of the cases, it is based on the analysis of the trajectories of moving objects in a controlled scene. The existing works rely on two phases. Firstly, they extract normal/abnormal clusters from saved trajectories through an unsupervised clustering algorithm. In the second phase, they consider a new detected trajectory and classify it as either normal or abnormal. In both phases, they need to compute similarity between trajectories. Thus, measuring such a similarity is a critical step while analyzing trajectories since it affects the quality of further applications such as clustering and classification. Despite the differences of the measured distances, authors claim the performance of the adopted distance. In this paper, we present a comparative experimental study on the efficiency of four distances widely used as trajectories’ similarity measure. Particularly, we examine the impact of the use of these distances on the quality of trajectory clustering. The experimental results demonstrate that the Longest Common SubSequence (LCSS) distance is the most accurate and efficient for the clustering task even in the case of different sampling rates and noise.