mono3DT [on] [gp] [3DT]
[Anonymous Submission]

Submitted on 20 Nov. 2018 08:41 by
[Anonymous Submission]

Running time:0.03 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
Vehicle 3D extents and trajectories are critical cues for
predicting the future location of vehicles and planning
future agent ego-motion based on those predictions. In this
paper, we propose a novel online framework for 3D vehicle
detec- tion and tracking from monocular videos. The
framework can not only associate detections of vehicles in
motion over time, but also estimate their complete 3D
bounding box infor- mation from a sequence of 2D images
captured on a moving platform. Our method leverages 3D
box depth-ordering matching for robust instance
association and utilizes 3D tra- jectory prediction for re-
identification of occluded vehicles. We also design a motion
learning module based on an LSTM for more accurate long-
term motion extrapolation. Our ex- periments on a
simulation dataset and the KITTI tracking dataset show that
our 3D tracking pipeline offers robust data association and
Latex Bibtex:
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 Detection and Tracking},
booktitle = {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.

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

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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