Method

Deep Structured Model [DSM]


Submitted on 17 May. 2017 16:11 by
Davi Frossard (University of Toronto)

Running time:0.1 s
Environment:GPU @ 1.0 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{frossard_tracking,
title={End-To-End Learning of Multi-Sensor 3D Tracking by Detection},
author={Frossard, Davi and Urtasun, Raquel},
booktitle={ICRA},
year={2018},
organization={IEEE}
}

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 60.05 % 64.09 % 57.18 % 67.22 % 83.64 % 59.91 % 86.32 % 85.39 %

Benchmark TP FP FN
CAR 27004 7388 637

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 73.94 % 83.50 % 76.67 % 939 60.98 %

Benchmark MT rate PT rate ML rate FRAG
CAR 59.38 % 32.15 % 8.46 % 737

Benchmark # Dets # Tracks
CAR 27641 1530

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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