Method

jerrymot [jerrymot]
[Anonymous Submission]

Submitted on 27 May. 2022 11:53 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
JDT
Parameters:
JDT
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 77.12 % 73.43 % 81.66 % 80.60 % 81.69 % 84.23 % 90.45 % 86.79 %
PEDESTRIAN 44.21 % 39.39 % 50.12 % 44.81 % 56.35 % 54.63 % 64.47 % 71.15 %

Benchmark TP FP FN
CAR 31787 2605 2147
PEDESTRIAN 14667 8483 3739

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.82 % 85.22 % 86.18 % 125 72.16 %
PEDESTRIAN 46.34 % 64.44 % 47.20 % 201 23.81 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.61 % 15.38 % 2.00 % 604
PEDESTRIAN 31.27 % 49.14 % 19.59 % 1530

Benchmark # Dets # Tracks
CAR 33934 1148
PEDESTRIAN 18406 983

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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