Method

mono3DT [on] [gp] [mono3DT]
https://github.com/ucbdrive/3d-vehicle-tracking

Submitted on 20 Jan. 2020 08:42 by
Hou-Ning Hu (Berkeley )

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

Method Description:
https://eborboihuc.github.io/Mono-3DT/
Parameters:
The full method with lstm, occ, depth
Latex Bibtex:
@inproceedings{Hu3DT19,
author = {Hu, Hou-Ning and Cai, Qi-Zhi and Wang, Dequan
and Lin, Ji and Sun, Min and Krähenbühl, Philipp and
Darrell, Trevor and Yu, Fisher},
title = {Joint Monocular 3D Vehicle Detection and
Tracking},
journal = {ICCV},
year = {2019}
}

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 73.16 % 72.73 % 74.18 % 76.51 % 85.28 % 77.18 % 87.77 % 86.88 %

Benchmark TP FP FN
CAR 30110 4282 745

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.28 % 85.45 % 85.38 % 379 71.55 %

Benchmark MT rate PT rate ML rate FRAG
CAR 73.08 % 24.00 % 2.92 % 573

Benchmark # Dets # Tracks
CAR 30855 936

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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