Syntactic matching of pedestrian trajectories for behavioral analysis

In the present work we propose a new approach to dynamically characterize trajectories for a syntactic spatio-temporal alignment that can be applied in the context of behavioral analysis and anomalous activity detection. The developed architecture is based on a symbolic representation of the trajectory, exploiting the framework of the so-called edit-distance. The acquired trajectory samples are filtered to identify the most significant spatio-temporal discontinuities: these key points are converted into a string-based domain where the matching of trajectory pairs can be expressed in terms of global alignment between symbols, similarly to DNA string matching algorithms. The extraction, characterization and alignment of trajectories have been tested in different environments, demonstrating the reliability of the achieved results and the viability of the solution for video surveillance and domotics applications.