Grouping motion trajectories
Samuel Pachoud, Emilio Maggio, Andrea Cavallaro · 2009
We present a method to group trajectories of moving objects extracted from real-world surveillance videos. The trajectories are first mapped into a low dimensionality feature space generated through linear regression. Next the regression coefficients are clustered by a Gaussian mixture model initialized by K-means for improved efficiency. The model selection problem is solved with Bayesian information criterion that penalizes models with high complexity. We demonstrate the proposed approach on both synthetic and real-world scenes. Experimental results show that the proposed clustering method outperforms K-means and mixture of regression models, while also reducing the computational complexity compared to the latter.