Unsupervised Fuzzy Clustering for Trajectory Analysis
Nadeem Anjum, Andrea Cavallaro · 2007
We propose an unsupervised fuzzy approach for motion trajectory clustering. The proposed approach is divided into three main steps: first Mean-shift is used for local mode seeking by analyzing trajectory data over multiple feature spaces. This step generates a set of tentative clusters. Next, adjacent clusters are combined by analysing the cluster attributes across all feature spaces. Sparse clusters are finally considered as generated by outlier object behaviors and then removed. The performance of the proposed algorithm is evaluated on real outdoor video surveillance scenarios with standard data-sets and it is compared with state-of-the-art techniques.