Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking
The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter is an analytic solution to the multi-target Bayes recursion used in multi-ta...
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MDPI AG
2025-03-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/4/84 |
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| author | M. Hadi Sepanj Saed Moradi Zohreh Azimifar Paul Fieguth |
| author_facet | M. Hadi Sepanj Saed Moradi Zohreh Azimifar Paul Fieguth |
| author_sort | M. Hadi Sepanj |
| collection | DOAJ |
| description | The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter is an analytic solution to the multi-target Bayes recursion used in multi-target tracking. It extends the Generalised Labelled Multi-Bernoulli (GLMB) framework by providing an efficient and scalable implementation while preserving track identities, making it a widely used approach in the field. Theoretically, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter handles uncertainties in measurements in its filtering procedure. However, in practice, degeneration of the measurement quality affects the performance of this filter. In this paper, we discuss the effects of increasing measurement uncertainty on the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter and also propose two heuristic methods to improve the performance of the filter in such conditions. The base idea of the proposed methods is to utilise the information stored in the history of the filtering procedure, which can be used to decrease the measurement uncertainty effects on the filter. Since GLMB filters have shown good results in the field of multi-target tracking, an uncertainty-immune <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB can serve as a strong tool in this area. In this study, the results indicate that the proposed heuristic ideas can improve the performance of filtering in the presence of uncertain observations. Experimental evaluations demonstrate that the proposed methods enhance track continuity and robustness, particularly in scenarios with low detection rates and high clutter, while maintaining computational feasibility. |
| format | Article |
| id | doaj-art-ddfa3842997846cabaaff883865770c4 |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-ddfa3842997846cabaaff883865770c42025-08-20T02:17:24ZengMDPI AGBig Data and Cognitive Computing2504-22892025-03-01948410.3390/bdcc9040084Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target TrackingM. Hadi Sepanj0Saed Moradi1Zohreh Azimifar2Paul Fieguth3Vision and Image Processing Laboratory, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaVision and Image Processing Laboratory, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaVision and Image Processing Laboratory, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaVision and Image Processing Laboratory, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaThe <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter is an analytic solution to the multi-target Bayes recursion used in multi-target tracking. It extends the Generalised Labelled Multi-Bernoulli (GLMB) framework by providing an efficient and scalable implementation while preserving track identities, making it a widely used approach in the field. Theoretically, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter handles uncertainties in measurements in its filtering procedure. However, in practice, degeneration of the measurement quality affects the performance of this filter. In this paper, we discuss the effects of increasing measurement uncertainty on the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB filter and also propose two heuristic methods to improve the performance of the filter in such conditions. The base idea of the proposed methods is to utilise the information stored in the history of the filtering procedure, which can be used to decrease the measurement uncertainty effects on the filter. Since GLMB filters have shown good results in the field of multi-target tracking, an uncertainty-immune <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>-GLMB can serve as a strong tool in this area. In this study, the results indicate that the proposed heuristic ideas can improve the performance of filtering in the presence of uncertain observations. Experimental evaluations demonstrate that the proposed methods enhance track continuity and robustness, particularly in scenarios with low detection rates and high clutter, while maintaining computational feasibility.https://www.mdpi.com/2504-2289/9/4/84multi-target trackingn-scan δ-GLMBrefined δ-GLMBmeasurement uncertainty effect reduction |
| spellingShingle | M. Hadi Sepanj Saed Moradi Zohreh Azimifar Paul Fieguth Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking Big Data and Cognitive Computing multi-target tracking n-scan δ-GLMB refined δ-GLMB measurement uncertainty effect reduction |
| title | Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking |
| title_full | Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking |
| title_fullStr | Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking |
| title_full_unstemmed | Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking |
| title_short | Uncertainty-Aware <i>δ</i>-GLMB Filtering for Multi-Target Tracking |
| title_sort | uncertainty aware i δ i glmb filtering for multi target tracking |
| topic | multi-target tracking n-scan δ-GLMB refined δ-GLMB measurement uncertainty effect reduction |
| url | https://www.mdpi.com/2504-2289/9/4/84 |
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