Method

Learning a Neural Solver for Multiple Object Tracking [MPNTrack]
https://github.com/dvl-tum/mot_neural_solver

Submitted on 15 Sep. 2021 11:20 by
Orcun Cetintas (Technical University of Munich)

Running time:0.02 s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
Graph Neural Network-based edge-classification for
data association.
Parameters:
All hyperparameters are specified in the code.
Latex Bibtex:
@InProceedings{braso_2020_CVPR,
author={Guillem Brasó and Laura Leal-Taixé},
title={Learning a Neural Solver for Multiple
Object Tracking},
booktitle = {The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

@article{MPNTrackSeg,
author = {Bras{\'o}, Guillem and Cetintas,
Orcun and Leal-Taix{\'e}, Laura},
date = {2022/09/26},
doi = {10.1007/s11263-022-01678-6},
id = {Bras{\'o}2022},
isbn = {1573-1405},
journal = {International Journal of Computer
Vision},
title = {Multi-Object Tracking and
Segmentation Via Neural Message Passing},
url = {https://doi.org/10.1007/s11263-022-
01678-6},
year = {2022}}

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
PEDESTRIAN 46.92 % 71.84 % 47.77 % 90.72 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 70.27 % 76.14 % 73.08 % 16415 5145 6946 46.25 % 26138 784

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 42.96 % 46.39 % 10.65 % 196 1151

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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