Label Metric for Multi-Class Multi-Target Tracking under Hierarchical Multilevel Classification

Abstract

Aiming at multiple quantities and types of targets, multi-class multi-target tracking usually faces not only cardinality errors, but also mis-classification problems. Considering its performance evaluation, the traditional optimal subpattern assignment (OSPA) method tends to calculate a separate metric for each class of targets, or introduce other indexes such as the classification error rate, which decreases the value of OSPA as a comprehensive single metric. This paper proposed a hierarchical multi-level class label for multi-class multi-target tracking under hierarchical multilevel classification, which can synthetically measure the state errors, cardinality error, and mis-classification. The hierarchical multi-level class label is introduced as an attached label to finite sets based on the hierarchical tree-structured categorization. A Wasserstein distance type metric then can be defined among the distribution represented by any two labels. The proposed label metric is a mathematic metric, and its advantages are illustrated by examples in several cases.