PartID – Individual Objects Tracking in Occupancy Grids Using Particle Identities

Abstract

Occupancy grid tracking algorithms see the world as made out of cells that can be either free or occupied, and can have speed probability densities attached to each cell. These algorithms estimate the overall state of the environment based on sensor data, but they are not aware of, nor concerned with the identity of individual objects. This paper proposes a new approach for individual objects tracking using the dynamic occupancy grids, which will embed the identity of the objects in the grid state. The particle based dynamic occupancy grid is extended by attaching identity information to each particle, thus achieving individual object tracking at grid level without the need of explicit modeling of the object’s shape. The position and dynamics of the world occupied cells are tracked independently of their identity, by the mechanism of the particle based occupancy grid. For achieving individual object tracking, this mechanism is extended with components for assigning and managing the identity of the particles. The designed system was tested on real world sequences, and was able to successfully track obstacles found on the road without making assumptions about their nature, shape or size.