Method

STMask+sipMask [STMask++]
[Anonymous Submission]

Submitted on 10 Dec. 2021 16:28 by
[Anonymous Submission]

Running time:19 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
It's a combine of STMask and sipmas. STMask is
spatial Feature calibration and Temporal Fusion for
Effective one-stage video instance segmentation
Parameters:
size_divsor = 32, flip_ratio=0.5
Latex Bibtex:
spatial Feature calibration and Temporal Fusion for
Effective one-stage video instance segmentation

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 51.90 % 52.52 % 52.40 % 61.41 % 69.77 % 58.55 % 75.76 % 82.12 %
PEDESTRIAN 27.11 % 25.07 % 31.00 % 28.26 % 54.57 % 38.12 % 54.79 % 72.43 %

Benchmark TP FP FN
CAR 26803 9957 5551
PEDESTRIAN 7366 13331 3351

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 56.83 % 79.74 % 57.81 % 360 42.06 %
PEDESTRIAN 18.39 % 68.77 % 19.40 % 209 7.28 %

Benchmark MT rate PT rate ML rate FRAG
CAR 44.74 % 43.84 % 11.41 % 990
PEDESTRIAN 12.96 % 43.70 % 43.33 % 723

Benchmark # Dets # Tracks
CAR 32354 860
PEDESTRIAN 10717 264

This table as LaTeX