Method

DisSiam_MOTS [DisSiam_MOTS]
[Anonymous Submission]

Submitted on 3 May. 2022 15:26 by
[Anonymous Submission]

Running time:10 s
Environment:1 core @ 2.5 Ghz（Python）

Method Description:
prediction model
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 58.72 % 75.25 % 46.33 % 80.47 % 85.71 % 67.44 % 58.32 % 88.02 %

Benchmark TP FP FN
CAR 33120 3640 1393

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 84.88 % 86.77 % 86.31 % 526 72.96 %

Benchmark MT rate PT rate ML rate FRAG
CAR 74.78 % 22.97 % 2.25 % 697

Benchmark # Dets # Tracks
CAR 34513 463

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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