A Multilevel Modeling Approach for Online Learning and Classification of Complex Trajectories for Video Surveillance

This paper proposes the Multilevel Gaussian Mixture Model (MLGMM), a novel algorithm for online, unsupervised modeling of complex motion trajectories which allows for fast and reliable abnormal trajectory detection in video surveillance. The presented approach does not directly deal with target tracking or attribution of an observation to either one or another trajectories belonging to different targets. Instead, it receives as an input a sequence of observations and considers them as produced by a reliable, yet possibly noisy, tracker. As an output, it then produces a labeling of the input sequence, as a "normal" or "abnormal" whole trajectory. The normal and abnormal trajectory domains are learned in an unsupervised fashion during the training phase and can be updated while the classifier is running, thanks to its reduced computational complexity. The novelty of the contribution is twofold. First, MLGMM is capable of representing short-term, as well as long-term, transitions in a consistent way, thus allowing for accurate representation of long and complex trajectories and their reliable labeling as normal or abnormal. Second, it allows the learning process to be run in parallel with the discrimination process, in order to support adaptability of the model to evolving operating conditions. Experimental results show that MLGMM outperforms a number of comparable approaches in the literature.