Method

Anonymous [Anonymous]
[Anonymous Submission]

Submitted on 27 Jun. 2022 04:41 by
[Anonymous Submission]

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
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 79.13 % 78.81 % 80.13 % 82.41 % 86.43 % 83.40 % 88.81 % 87.11 %
PEDESTRIAN 52.72 % 53.55 % 52.21 % 58.87 % 70.15 % 59.50 % 65.00 % 77.69 %

Benchmark TP FP FN
CAR 32256 2136 537
PEDESTRIAN 17759 5391 1670

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.72 % 85.74 % 92.23 % 173 78.35 %
PEDESTRIAN 68.37 % 73.60 % 69.50 % 262 48.12 %

Benchmark MT rate PT rate ML rate FRAG
CAR 85.85 % 11.54 % 2.62 % 172
PEDESTRIAN 51.55 % 34.36 % 14.09 % 741

Benchmark # Dets # Tracks
CAR 32793 731
PEDESTRIAN 19429 336

This table as LaTeX


This figure as: png pdf

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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