Video browsing using object trajectories
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
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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.