Method

MaskguidedMOTS [on] [MG-MOTS]


Submitted on 29 Sep. 2022 15:33 by
Jin Seong (Hanyang University )

Running time:41 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Centermask(Instance Segmentation) + Mask-Guided ReID
Branch(Association)
: online tracker & real-time method
Parameters:
centermaskWithMaskHeadReid_v39_lite_uncertaintyLoss
_ft_mix_w_ID_11k_conv2dhead_th05_dim256_embth05_fre
eze_addGN_p3p7_fulltrain
Latex Bibtex:
@article{SEONG2023144,
title = {Online and real-time mask-guided multi-
person tracking and segmentation},
journal = {Pattern Recognition Letters},
volume = {172},
pages = {144-150},
year = {2023},
issn = {0167-8655},
doi =
{https://doi.org/10.1016/j.patrec.2023.06.001},
url =
{https://www.sciencedirect.com/science/article/pii
/S0167865523001733},
author = {Jin Seong},
keywords = {Multi-object tracking, Multi-object
tracking and segmentation, Deep learning,
Autonomous driving, Re-identification, Real-time
tracking}}

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 0.00 % 0.00 % 0.00 % 0.00 % 0.00 %
PEDESTRIAN 54.40 % 70.80 % 78.50 % 72.50 % 93.50 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 0.00 % 0.00 % 0.00 % 0 0 0 0.00 % 0 0
PEDESTRIAN 76.10 % 95.40 % 84.70 % 15750 754 4947 6.80 % 19166 646

Benchmark MT PT ML IDS FRAG
CAR 0.00 % 0.00 % 0.00 % 0 0
PEDESTRIAN 41.50 % 38.90 % 19.60 % 351 737

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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