Method

QD-3DT [on] [QD-3DT]
https://eborboihuc.github.io/QD-3DT

Submitted on 7 Dec. 2020 20:25 by
Hou-Ning Hu (Berkeley )

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
End-to-end 3D detection and tracking
Parameters:
None
Latex Bibtex:
@article{Hu2021QD3DT,
author = {Hu, Hou-Ning and Yang, Yung-Hsu and
Fischer, Tobias and Yu, Fisher and Darrell, Trevor
and Sun, Min},
title = {Monocular Quasi-Dense 3D Object Tracking},
journal = {ArXiv:2103.07351},
year = {2021}
}

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 72.77 % 74.09 % 72.19 % 78.13 % 85.48 % 74.87 % 89.21 % 87.16 %
PEDESTRIAN 41.08 % 44.01 % 38.82 % 48.96 % 67.19 % 42.09 % 72.44 % 77.38 %

Benchmark TP FP FN
CAR 30599 3793 836
PEDESTRIAN 14786 8364 2084

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.94 % 85.78 % 86.54 % 206 73.29 %
PEDESTRIAN 51.77 % 73.13 % 54.87 % 717 34.61 %

Benchmark MT rate PT rate ML rate FRAG
CAR 75.23 % 21.85 % 2.92 % 525
PEDESTRIAN 32.65 % 48.11 % 19.24 % 1194

Benchmark # Dets # Tracks
CAR 31435 1098
PEDESTRIAN 16870 1147

This table as LaTeX