Method

IMOU_ALG [IMOU_ALG]


Submitted on 30 Dec. 2022 11:21 by
Zion Ma (Zhejiang University)

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

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 82.08 % 78.78 % 86.21 % 84.83 % 83.98 % 90.00 % 89.84 % 87.14 %
PEDESTRIAN 57.15 % 55.38 % 59.67 % 60.38 % 70.63 % 65.71 % 71.80 % 77.42 %

Benchmark TP FP FN
CAR 33326 1066 1414
PEDESTRIAN 18377 4773 1413

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 92.75 % 85.61 % 92.79 % 12 78.81 %
PEDESTRIAN 72.20 % 72.98 % 73.28 % 250 50.75 %

Benchmark MT rate PT rate ML rate FRAG
CAR 90.15 % 4.46 % 5.38 % 64
PEDESTRIAN 52.58 % 36.77 % 10.65 % 593

Benchmark # Dets # Tracks
CAR 34740 649
PEDESTRIAN 19790 329

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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