Method

InvariantGraphMOT [InvariantGraphMOT]
[Anonymous Submission]

Submitted on 12 Dec. 2021 02:16 by
[Anonymous Submission]

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
GNN on a graph with invariant edge features and no node features
Parameters:
TBA
Latex Bibtex:
TBA

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 75.16 % 73.94 % 76.95 % 80.81 % 82.40 % 80.00 % 89.27 % 87.12 %
PEDESTRIAN 43.59 % 39.88 % 48.12 % 44.90 % 57.40 % 51.95 % 65.22 % 71.34 %

Benchmark TP FP FN
CAR 31724 2668 2003
PEDESTRIAN 14628 8522 3481

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.08 % 85.63 % 86.42 % 462 71.82 %
PEDESTRIAN 46.98 % 64.59 % 48.15 % 270 24.61 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.92 % 16.61 % 2.46 % 599
PEDESTRIAN 29.90 % 51.20 % 18.90 % 1554

Benchmark # Dets # Tracks
CAR 33727 1205
PEDESTRIAN 18109 1009

This table as LaTeX