CONICET | Buscador de Institutos y Recursos Humanos

Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity modeling its trajectory and speed, and using a state machine. The particularity of our methodology is the use of a Movement Feature Space (MFS) to generate descriptors for the classifier and the tracker. This approach is applied to two public sequences (PETS2009 and TownCentre). The tracking results outperform other algorithms reported in the literature, which have, however, a higher computational complexity.