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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 89.91 % 85.50 % 89.97 % 88.35 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 91.13 % 99.60 % 95.18 % 34043 137 3313 1.23 % 38015 780

Benchmark MT PT ML IDS FRAG
CAR 77.85 % 18.15 % 4.00 % 21 259

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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