Method

MKBT [on] [MKBT]
[Anonymous Submission]

Submitted on 20 Jul. 2026 11:17 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 3.5 Ghz (Python)

Method Description:
3DMambaTrack is an online 3D multi-
object tracking framework that combines LiDAR-
based 3D detections with learned temporal motion
modeling.
Parameters:
Virconv detections
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 82.41 % 78.84 % 86.76 % 83.00 % 86.70 % 89.80 % 91.27 % 88.08 %

Benchmark TP FP FN
CAR 32121 2271 802

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.03 % 86.86 % 91.06 % 11 78.76 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.08 % 4.92 % 8.00 % 51

Benchmark # Dets # Tracks
CAR 32923 637

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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