<title>Tracking with classification-aided multiframe data association</title>

Abstract

In most conventional tracking systems, only the target kinematic information is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. In addition, the use of target class information in data association can improve discrimination by yielding purer tracks and preserve their continuity. In this paper, we present the integrated use of target classification information and target kinematic information for target tracking. In our approach, target class information is integrated into the data association process using the two-dimensional (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The latter is an optimization based MHT. A generic model of the classifier output is considered and its use in association likelihoods is discussed. The multiframe association likelihood is developed to include the classification results based on the confusion matrix that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparison with and without the use of class information in data association is presented on a ground target tracking problem where targets are moving in an open field and their tracks can merge, branch and cross. Simulation results quantify the benefits of classification aided data association for improved target tracking, especially in the presence of association uncertainty in kinematic measurements. Also the benefit of S-D (multiframe) association vs. 2-D association is investigated for different quality classifiers. The main contribution is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.