Method

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


Submitted on 17 Apr. 2025 09:59 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 79.56 % 77.27 % 82.47 % 80.59 % 86.49 % 85.22 % 89.71 % 87.08 %
PEDESTRIAN 53.63 % 50.39 % 57.33 % 54.77 % 71.76 % 61.65 % 74.33 % 78.66 %

Benchmark TP FP FN
CAR 31676 2716 371
PEDESTRIAN 16392 6758 1278

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.56 % 85.65 % 91.02 % 160 77.34 %
PEDESTRIAN 64.21 % 74.72 % 65.29 % 249 46.31 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.77 % 14.15 % 3.08 % 296
PEDESTRIAN 41.58 % 40.89 % 17.53 % 667

Benchmark # Dets # Tracks
CAR 32047 803
PEDESTRIAN 17670 459

This table as LaTeX

This figure as: png pdf

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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