Method

MaskguidedMOTS [on] [MaskguidedMOTS]
[Anonymous Submission]

Submitted on 1 Sep. 2021 17:33 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
CenterMask + reid branch(roi&mask feature)
{HYU CVLAB,Jin Seong}
Parameters:
exp-name
centermaskWithMaskHeadReid_v39_lite_uncertaintyL
oss
_ft_mix_w_ID_11k_conv2dhead_th05_dim256_embth05_
fre
eze_addGN_p3p7_fulltrain

MODEL.WEIGHTS
./output/weight/centermask+maskheadreid_p3p7_V_3
9_l
ite_eSE_FPN_ms_3x_conv2dhead_(COCOperson-
>KITTI+MOTS)_dim256_gtid_train_val_freeze/model_
001
0999.pth
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