Method

[on] Online MOTS using Simple Affinity Fusion [GMPHD_SAF]


Submitted on 29 May. 2020 22:08 by
Young-min Song (Gwanju Institute of Science and Technology)

Running time:0.08 s
Environment:4 cores @ 4.2 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{gmphdsaf,
title={Online Multi-Object Tracking and Segmentation
with GMPHD Filter and Simple Affinity Fusion},
author={Young-min Song and Moongu Jeon},
journal={arXiv preprint arXiv:2009.00100},
year={2020}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics (adapted for the segmentation case): CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark sMOTSA MOTSA MOTSP MODSA MODSP
CAR 75.40 % 86.70 % 87.50 % 88.20 % 90.10 %
PEDESTRIAN 62.80 % 78.20 % 81.60 % 80.50 % 93.70 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.80 % 97.20 % 93.90 % 33387 964 3373 8.70 % 49145 574
PEDESTRIAN 83.20 % 96.80 % 89.50 % 17223 570 3474 5.10 % 23551 357

Benchmark MT PT ML IDS FRAG
CAR 82.00 % 17.40 % 0.60 % 549 874
PEDESTRIAN 59.30 % 35.90 % 4.80 % 474 696

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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