People tracking is a basic capability in almost any robotic application. So it is in robotic competitions, where many robot skills rely on this ability. This problem is still challenging, particularly when are implemented using low definition sensors as Laser Imaging Detection and Ranging (LIDAR) sensors in crowded environments. This paper describes a solution based on a single LIDAR sensor that uses the gait to keep a continuous identification in time and space of the individual. The system described in this article is based on PeTra (People Tracking) package, which uses convolutional neural networks to identify legs in populated environments. Experimental validation proposes a test in an apartment replicating realistic competition arena.