Syntactic Matching of Trajectories for Ambient Intelligence Applications

In this paper we propose a novel approach for syntactic description and maching of object trajectories in digital video, suitable for classification and recognition purposes. Trajectories are first segmented by detecting the meaningful discontinuities in time and space, and are successively expressed through an adhoc syntax. A suitable metric is then proposed, which allows determining the similarity among trajectories, based on the so-called inexact or approximate matching. The metric mimics the algorithms used in bioinformatics to match DNA sequences, and returns a score, which allows identifying the analogies among different trajectories on both global and local basis. The tool can therefore be adopted for the analysis, classification, and learning of motion patterns, in activity detection or behavioral understanding.