Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913893100552192 |
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| author | Xia, Yuxuan García-Fernández, Ángel F. Karlsson, Johan Chang, Kuo-Chu Yuan, Ting Svensson, Lennart |
| author_facet | Xia, Yuxuan García-Fernández, Ángel F. Karlsson, Johan Chang, Kuo-Chu Yuan, Ting Svensson, Lennart |
| contents | This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inherent uncertainty in probabilistic multi-object representations. Additionally, P-GOSPA retains the interpretability of GOSPA, such as its decomposition into localization, missed detection, and false detection errors in a sound and meaningful manner. Examples and simulations are provided to demonstrate the efficacy of the proposed P-GOSPA metric. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_11482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties Xia, Yuxuan García-Fernández, Ángel F. Karlsson, Johan Chang, Kuo-Chu Yuan, Ting Svensson, Lennart Signal Processing This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inherent uncertainty in probabilistic multi-object representations. Additionally, P-GOSPA retains the interpretability of GOSPA, such as its decomposition into localization, missed detection, and false detection errors in a sound and meaningful manner. Examples and simulations are provided to demonstrate the efficacy of the proposed P-GOSPA metric. |
| title | Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.11482 |