Method

NTU-Tracker [NTU-Tracker]
[Anonymous Submission]

Submitted on 19 May. 2025 10:59 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
a new tracker with new representation and metrics
Parameters:
na
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 82.75 % 79.45 % 86.79 % 83.23 % 87.16 % 89.88 % 91.19 % 88.04 %

Benchmark TP FP FN
CAR 32233 2159 608

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.87 % 86.80 % 91.95 % 29 79.50 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.54 % 4.46 % 8.00 % 58

Benchmark # Dets # Tracks
CAR 32841 652

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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