Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Abstract

Performance evaluation (PE) plays a key role in the design and validation of any target-tracking algorithms. In fact, it is often closely related to the definition and derivation of the optimality/suboptimality of an algorithm such as that all minimum mean-squared error estimators are based on the minimization of the mean-squared error of the estimation. In this paper, we review both classic and emerging novel PE metrics and approaches in the context of estimation and target tracking. First, we briefly review the evaluation metrics commonly used for target tracking, which are classified into three groups corresponding to the most important three factors of the tracking algorithm, namely correctness, timeliness, and accuracy. Then, comprehensive evaluation (CE) approaches such as cloud barycenter evaluation, fuzzy CE, and grey clustering are reviewed. Finally, we demonstrate the use of these PE metrics and CE approaches in representative target tracking scenarios.

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

“…Because the number of elements of the real set and estimated set of the states of the targets is different, the root-mean-square type of evaluation (such as the root mean square error) is not usable. In this article, optimal sub-pattern assignment (OSPA) error is selected to evaluate the multi-target tracking performance, which is defined as follows [41]:…”

CoPreMo: A Collaborative Predictive Model in Time Series and its Application to Radar Target Tracking for ADAS/AD Vehicles

“…Different performance measures can be used to evaluate a tracking process as explained in [19], [20]. These include statistical measures such as accuracy and error measures used to evaluate the statistical performance of a model, and system performance measures such as time and space complexities used to evaluate the computational performance of a model.…”