Method

Kalman-based Multi-Object Tracking with Adaptive Uncertainty Learning [K-MOT-AUL]


Submitted on 23 May. 2025 15:37 by
Mengjun Chen (BIT)

Running time:1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
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Parameters:
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Latex Bibtex:
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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 79.59 % 77.27 % 82.53 % 80.55 % 86.58 % 85.23 % 89.79 % 87.09 %

Benchmark TP FP FN
CAR 31651 2741 348

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.55 % 85.66 % 91.02 % 160 77.36 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.46 % 14.46 % 3.08 % 294

Benchmark # Dets # Tracks
CAR 31999 789

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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