Tracking Multiple Objects Using a Kalman Filter and a Probabilistic Association Process

In this paper one of the most important solutions in position estimation is used in conjunction with a data association algorithm in order to achieve a multi-tracking application. A Kalman filter is extended and adapted in order to track the position and speed of a variable number of objects in an unstructured and complex environment. Both the developed algorithms and the results obtained with their real-time execution implementation in the mentioned application are described, and interesting conclusions extracted from these experiments are remarked in the paper. Finally, tracking results of the proposed algorithm are compared with another multi-object estimator based on a particle filter previously developed by the authors.