Multi-object tracking at intersections using the cardinalized probability hypothesis density filter

A large percentage of accidents with body injuries in urban areas occur at intersections. Thus, improving safety at intersections using infrastructure based perception systems is desirable. In order to recognize and track the moving objects, a network of laserscanners is used to observe the intersection. In this contribution, a robust object recognition algorithm for vehicles and pedestrians is proposed. Further, the Cardinalized Probability Hypothesis Density with integrated estimation of the clutter density is applied to track vehicles and pedestrians at an intersection. The performance of the system is evaluated using real world sensor data of an intersection.