Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes

This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by correct clustering rate (CCR) and time cost (TC). The experimental results demonstrate that in outdoor surveillance scenes, the simpler PCA+Euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as DTW, LCSS are not efficient due to their high computational cost