A new data association method for 3-D object tracking in automotive applications

Abstract

We present a new method for data association in 3-D object tracking for automotive applications. The method is a variant of the nearest-neighbor data association and is based on comparing the location of an existing track with that of each incoming object and associating to the one which is closest in 3-D space. As a pair is associated, it is removed from the search space and the association process continues until all assignments are made. Our experiments show that the proposed method significantly reduces the processing cost as compared to the existing full-search nearest-neighbor method and maintains similar performance at the signal to noise ratios that are typically encountered in automotive object tracking. We will provide guidelines on selecting the operating parameters and suggestions on handling the case when the number of incoming objects is not equal to the number of existing tracks.