[Submitted on 13 Sep 2026]
Title:Causal neural set filtering for online multi-target tracking
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Abstract:Transformer-based multi-target tracking (MTT) jointly learns data association and state estimation, but MT3/Track-MT3-style trackers repeatedly re-encode measurement windows, incurring redundant computation. We propose Causal Neural Set Filtering (CNSF)\footnote{\href{this https URL}{Code: this https URL}}, a neural set filter that encodes only current measurements while carrying past evidence in a structured recursive track state. CNSF combines exclusive Sinkhorn association, association-conditioned Kalman-shaped updates with moment matching, and recurrent Bernoulli lifecycle modeling with measurement-driven birth. These mechanisms impose soft one-to-one constraints, propagate association-induced state uncertainty, and support existence estimation under missed detections and birth--death transitions. On a held-out three-regime simulated test set, CNSF reduces mean GOSPA and T-GOSPA relative to Track-MT3 by 19.3\% and 30.4\%, with 55.9\% fewer parameters and a $3.76\times$ speedup in single-thread CPU inference.
Comments: 5 pages, 2 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.16054 [cs.LG]
(or arXiv:2609.16054v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16054
arXiv-issued DOI via DataCite
Submission history
From: Huangyu Dai [view email] [v1] Sun, 13 Sep 2026 05:31:49 UTC (636 KB)
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