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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 84.52 % 85.64 % 85.62 % 88.62 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.81 % 97.95 % 93.15 % 33661 705 4242 6.34 % 38507 1227

Benchmark MT PT ML IDS FRAG
CAR 73.38 % 23.85 % 2.77 % 377 847

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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