Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters

In this paper we present a stereovision-based approach for tracking multiple objects in crowded environments where, typically, the road lane markings are not visible and the surrounding infrastructure is not known. The proposed technique relies on measurement data provided by an intermediate occupancy grid derived from processing a stereovision-based elevation map and on free-form object delimiters extracted from this grid. Unlike other existing methods that track rigid objects using also rigid representations, we present a particle filter-based solution for tracking visual appearance-based free-form obstacle representations. At each step, the particle state is described by two components, i.e., the object's dynamic parameters and its estimated geometry. In order to solve the high-dimensionality state-space problem, a Rao-Blackwellized particle filter is used. By accurately modeling the object geometry using the polygonal lines instead of a 3-D box and, at the same time, separating the position and speed tracking from the geometry tracking at the estimator level, the proposed solution combines the efficiency of the rigid model with the benefits of a flexible object model.