Correction: BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Hyper-parameters I oU , Mh D and shape are tuned to work with the original implementation and corrected implementation results in slightly worse results.We provide the corrected versions of Tables 1, 2 and 3 from the paper.Note that even the results of the settings that do not depend on S shape are slightly different from the original paper due to the different Python package versions.

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Publication Details

Journal
Machine Vision and Applications
Published
2024-08-31
DOI
https://doi.org/10.1007/s00138-024-01605-4
Citations
2
Primary Topic
Video Surveillance and Tracking Methods
Type
article
Field-Weighted Citation Impact
0.50
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article

Correction: BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Branimir Todorović, Vukasin D. Stanojevic
2 citations
Machine Vision and Applications
Video Surveillance and Tracking Methods
0.50
article

Correction: BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Branimir Todorović, Vukasin D. Stanojevic
article en
2 citations

Abstract

Hyper-parameters I oU , Mh D and shape are tuned to work with the original implementation and corrected implementation results in slightly worse results.We provide the corrected versions of Tables 1, 2 and 3 from the paper.Note that even the results of the settings that do not depend on S shape are slightly different from the original paper due to the different Python package versions.

Machine Vision and ApplicationsVol. 35(5)
University of Nis (RS)
Openalex Percentile: Top 36%
Video Surveillance and Tracking Methods
0.50
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