Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application

Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.