Method

EAFFMOT[on][la] [EAFFMOT]


Submitted on 20 Jul. 2022 12:09 by
Jingyi Jin (Jilin University)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
3D multi-object tracking with a new data association
and an improved trajectory management mechanism
Parameters:
python=3.6
Latex Bibtex:
EAFFMOT

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 71.97 % 71.89 % 72.53 % 76.92 % 83.82 % 75.89 % 88.54 % 86.72 %
PEDESTRIAN 39.81 % 35.47 % 44.90 % 38.45 % 59.46 % 48.53 % 63.17 % 71.24 %

Benchmark TP FP FN
CAR 30400 3992 1162
PEDESTRIAN 12402 10748 2568

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.66 % 85.06 % 85.01 % 121 71.46 %
PEDESTRIAN 41.45 % 64.56 % 42.48 % 239 22.46 %

Benchmark MT rate PT rate ML rate FRAG
CAR 70.77 % 19.23 % 10.00 % 289
PEDESTRIAN 23.02 % 40.55 % 36.43 % 1144

Benchmark # Dets # Tracks
CAR 31562 797
PEDESTRIAN 14970 385

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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