On the use of a minimal path approach for target trajectory analysis

Vision-based action recognition has multiple applications, mainly focused in video surveillance systems. The art of labeling each target behavior in crowded scenarios is a complicated field since usually we do not have visual confirmation of the parts of a target to infer its behavior. Thus, trajectory analysis becomes a good choice to try to infer knowledge about target movements. Most of the contributions to this field involve a training period in which we obtain information a priori about the environment, storing a dataset with all the possible usual routes. Based in the minimal path theory using geodesic active contours, we present a novel architecture where no initial information about the scene is needed, while it is possible to include it if necessary to specify constraints. Experimental results in two different application domains show the performance and flexibility of this method, being able to be used in multiple trajectory analysis problems.

► We model a system for path analysis using minimal paths.
► Dynamic learning is achieved without high memory issues.
► No information a priori is needed to obtain the usual routes.
► Different cases of study can be solved modifying the potential image properties.
► Quantitative metric analysis for trajectory similarities is presented.