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 + Mask-Guided ReID Branch
: 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:

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
PEDESTRIAN 56.85 % 57.81 % 56.61 % 61.63 % 77.28 % 60.98 % 78.93 % 81.07 %

Benchmark TP FP FN
PEDESTRIAN 15753 4944 751

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
PEDESTRIAN 70.78 % 78.46 % 72.48 % 352 54.39 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 41.48 % 38.89 % 19.63 % 718

Benchmark # Dets # Tracks
PEDESTRIAN 16504 444

This table as LaTeX

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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