Method

[on] MOTS with Embedding Mask-based Affinity Fusion in Hierarchical Data Association [MAF_HDA]
https://github.com/SonginCV/MAF_HDA

Submitted on 10 Dec. 2021 02:50 by
Young-min Song (Gwanju Institute of Science and Technology)

Running time:0.09 s
Environment:4 cores @ 4.2 Ghz (C/C++)

Method Description:
Proposed method has worked in Intel i7 CPU, 4 cores
@ 4.2 Ghz, and w/o any GPU acceleration.
Parameters:
TBD
Latex Bibtex:
@article{mafhda,
title={Multi-Object Tracking and Segmentation
with Embedding Mask-based Affinity Fusion in
Hierarchical Data Association},
author={Y. Song and Y. Yoon and K. Yoon and M.
Jeon},
journal={IEEE Access},
pages={1-1},
month=may,
year={2022}
}

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 70.00 % 78.39 % 62.96 % 82.43 % 88.81 % 67.41 % 84.26 % 89.44 %
PEDESTRIAN 57.99 % 66.34 % 51.69 % 69.87 % 82.73 % 57.89 % 75.25 % 84.43 %

Benchmark TP FP FN
CAR 33396 3364 723
PEDESTRIAN 17132 3565 347

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 87.75 % 88.37 % 88.88 % 415 77.19 %
PEDESTRIAN 79.64 % 82.31 % 81.10 % 301 65.00 %

Benchmark MT rate PT rate ML rate FRAG
CAR 81.98 % 17.27 % 0.75 % 585
PEDESTRIAN 57.78 % 35.93 % 6.30 % 424

Benchmark # Dets # Tracks
CAR 34119 964
PEDESTRIAN 17479 440

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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