Method

Actionable Uncertainty for Reliable Tracking [AURTrack_online]
[Anonymous Submission]

Submitted on 26 Aug. 2026 09:46 by
[Anonymous Submission]

Running time:11.3 s
Environment:>8 cores @ >3.5 Ghz (Python)

Method Description:
An online 3D multi-object tracking framework
centered on uncertainty-aware state estimation. It
adapts motion and observation uncertainty during
tracking, uses uncertainty cues to suppress
unreliable state updates, and combines 2D/3D
evidence with confidence-aware trajectory
management.
Parameters:
3D association cost thresholds: 1.6 and 1.0;
initial XY Kalman-filter covariance: 1.0.
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 78.92 % 76.17 % 82.50 % 79.02 % 87.05 % 85.06 % 90.69 % 87.01 %

Benchmark TP FP FN
CAR 31068 3324 148

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.77 % 85.51 % 89.91 % 48 76.67 %

Benchmark MT rate PT rate ML rate FRAG
CAR 77.85 % 18.15 % 4.00 % 264

Benchmark # Dets # Tracks
CAR 31216 669

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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