Transformer-based assignment decision network for multiple object tracking-Reference-Cited by-同舟云学术

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Transformer-based assignment decision network for multiple object tracking

Published:2024-04 Issue: Volume:241 Page:103957
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Psalta Athena^ORCID,Tsironis Vasileios,Karantzalos Konstantinos

Funder

National Technical University of Athens

Publisher

Elsevier BV

Reference83 articles.

1. Bae, S.-H., Yoon, K.-J., 2014. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1218–1225.

2. Bergmann, P., Meinhardt, T., Leal-Taixe, L., 2019. Tracking without bells and whistles. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 941–951.

3. Evaluating multiple object tracking performance: the clear mot metrics;Bernardin;EURASIP J. Image Video Process.,2008

4. Simple online and realtime tracking;Bewley,2016

5. Bochinski, E., Eiselein, V., Sikora, T., 2017. High-Speed Tracking-by-Detection Without Using Image Information. In: International Workshop on Traffic and Street Surveillance for Safety and Security At IEEE AVSS 2017. Lecce, Italy.

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1. Deep SORT Related Studies;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-04-19

2. Improving Object Detection in Videos: A Comprehensive Evaluation of Faster R-CNN Employed in Partial Occlusion Handling;2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA);2024-03-01

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