Method

Multi-Hierarchy Fusion and Occlusion Estimator for Multiple Object Tracking [on] [MHF-SOE]
[Anonymous Submission]

Submitted on 1 Nov. 2023 13:07 by
[Anonymous Submission]

Running time:1247 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Multi-Hierarchy Fusion for feature association; SOE
for location association.
Parameters:
None
Latex Bibtex:
None

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 72.80 % 70.85 % 75.62 % 75.44 % 81.30 % 79.97 % 84.33 % 84.13 %

Benchmark TP FP FN
CAR 30873 3519 1044

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.13 % 82.06 % 86.73 % 208 70.02 %

Benchmark MT rate PT rate ML rate FRAG
CAR 72.77 % 22.77 % 4.46 % 321

Benchmark # Dets # Tracks
CAR 31917 720

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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