Method

CenterTube_RCNN [CenterTube_RCNN]
[Anonymous Submission]

Submitted on 28 May. 2022 15:51 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
3D MOT Tracker
Parameters:
alpha=0.2
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 71.25 % 74.27 % 69.24 % 79.94 % 83.53 % 72.01 % 90.28 % 86.85 %

Benchmark TP FP FN
CAR 31507 2885 1406

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.97 % 85.19 % 87.52 % 191 73.40 %

Benchmark MT rate PT rate ML rate FRAG
CAR 78.46 % 14.15 % 7.38 % 344

Benchmark # Dets # Tracks
CAR 32913 846

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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