Generating background NPCs motion and grouping behavior based on real video sequences

Nowadays, there are many different types of NPCs (Non-Playable Character) in games, which can be endowed with different abilities, levels of intelligence and more or less realistic motion. How much the NPCs abilities are sophisticated, more complex are techniques to model and control them. Concerning the realism of NPCs motion, the ideal result is to provide characters that act and move in a way that seems natural to people. However, it can take a long time and a lot of work for developers, since designers should define characteristics for each different background character. This work aims to introduce a trajectory learning system derived from real video sequences, which can be used to generate NPCs motion for games. In addition to NPCs trajectories, we propose to detect in video sequences the formed groups and its characteristics to be used to simulate more realistic NPCs. We performed some evaluations using a pre-defined set of real video sequences and compared them with generated simulations. The obtained results indicate that our motion learning and groups detection techniques can be a valuable resource in order to generate NPCs and groups of NPCs trajectories, in an automatic way and similar to the real-life.