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Review
. 2022 Jan 20;22(3):793.
doi: 10.3390/s22030793.

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Yan Song  1 , Zheng Hu  1 , Tiancheng Li  1 , Hongqi Fan  2
Affiliations
Review

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Yan Song et al. Sensors (Basel). .

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.

Keywords: cloud barycenter evaluation; fuzzy CE; grey clustering; performance evaluation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Classification of representative CE metrics.
Figure 2
Figure 2
Mapping the tracker hypotheses to objects. In the easiest case, different associations result in evaluation metrics.
Figure 3
Figure 3
Qualitative evaluation of the cloud-generator model.
Figure 4
Figure 4
PE of cloud gravity for target tracking.
Figure 5
Figure 5
PE of target tracking based on fuzzy CE.
Figure 6
Figure 6
PE for target tracking based on grey clustering.
See this image and copyright information in PMC

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