In this paper, we propose a new approach of indexing trajectories to efficiently distinguish abnormal behaviors from normal ones. After a discretization step, trajectories are considered as sets of triplets (location, velocity, direction). Those triplets are seen as words and a multinomial modeling is learned to estimate the probability of each word. The originality of our work consists in computing the likelihood of all measures and aggregating them by trajectories and spatial cells. The achieved representation is light and offers new opportunities to request normal or abnormal behaviors. The interest of our approach is demonstrated on a plane trajectory dataset provided by Paris-Charles de Gaulle airport. Several experiments are carried out to promote the proposed likelihood descriptors; in particular, experiments show how to extract easily relevant specific trajectories. A t-SNE diagram is also presented to achieve an overall discriminative representation of the whole dataset.
Trajectory Bayesian indexing: The airport ground traffic case
2016-11-01
2343402 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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