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 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 76.15 % 83.42 % 77.01 % 86.73 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 80.23 % 98.09 % 88.27 % 29736 578 7328 5.20 % 33287 2094

Benchmark MT PT ML IDS FRAG
CAR 60.00 % 31.69 % 8.31 % 296 868

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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