Unsupervised trajectory modeling based on discrete descriptors for classifying moving objects in video sequences

This paper focuses on modeling and classifying trajectories from video sequences. Location, velocity and time of appearance are considered as features for recognizing and modeling motions of objects. In a training phase, a discretization of the proposed features is performed by using a self-organizing map approach such that a set of clusters (feature vocabulary) is created for describing trajectories. A cluster dissimilarity measure based on a weighted fusion of features facilitates the recognition of trajectory classes in an incremental way. As a result, an unsupervised method for encoding observed motion information and identifying trajectory patterns is proposed in this article. The method is evaluated with real and simulated data. Additionally' comparisons with previous works show the benefits of our method when encoding and identifying motion patterns in video sequences.