FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s11263-021-01513-4.pdf
Reference99 articles.
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2. Bae, S. H., & Yoon, K. J. (2017). Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), 595–610.
3. Berclaz, J., Fleuret, F., Turetken, E., & Fua, P. (2011). Multiple object tracking using k-shortest paths optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9), 1806–1819.
4. Bergmann, P., Meinhardt, T., & Leal-Taixe, L. (2019). Tracking without bells and whistles. In ICCV (pp. 941–951).
5. Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The clear mot metrics. EURASIP Journal on Image and Video Processing, 2008, 1–10.
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