research-article

Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking

Authors:

Liang ZhengAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 31

Pages 612 - 622

https://doi.org/10.1109/TIP.2021.3131936

Published: 01 January 2022 Publication History

Abstract

Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might not be the best choice given the difference in matching scopes between re-ID and MTMCT problems. Re-ID systems focus on <italic>global matching</italic>, which retrieves targets from all cameras and all times. In contrast, data association in tracking is a <italic>local matching</italic> problem, since its candidates only come from neighboring locations and time frames. In this paper, we design experiments to verify such misfit between <italic>global</italic> re-ID feature distances and <italic>local</italic> matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT. Instead of trying to deal with all appearance changes, we tailor the affinity metric to specialize in ones that might emerge during data associations. To this end, we introduce a new data sampling scheme with temporal windows originally used for data associations in tracking. Minimizing the mismatch, the adaptive affinity module brings significant improvements over global re-ID distance, and produces competitive performance on CityFlow and DukeMTMC datasets.

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Cited By

Shim KKo KHwang JJang HKim C(2023)Fast online multi-target multi-camera tracking for vehiclesApplied Intelligence10.1007/s10489-023-05081-753:23(28994-29004)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05081-7
Gao ZHuang Z(2022)Global-View Re-identification Tracking with TransformerArtificial Intelligence10.1007/978-3-031-20497-5_51(622-633)Online publication date: 27-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-20497-5_51

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 31, Issue

2022

3518 pages

ISSN:1057-7149

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Cited By

Shim KKo KHwang JJang HKim C(2023)Fast online multi-target multi-camera tracking for vehiclesApplied Intelligence10.1007/s10489-023-05081-753:23(28994-29004)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05081-7
Gao ZHuang Z(2022)Global-View Re-identification Tracking with TransformerArtificial Intelligence10.1007/978-3-031-20497-5_51(622-633)Online publication date: 27-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-20497-5_51

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