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 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
PEDESTRIAN 45.26 % 43.74 % 47.28 % 53.62 % 58.30 % 52.18 % 68.47 % 75.93 %

Benchmark TP FP FN
PEDESTRIAN 16194 6956 5096

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 46.23 % 71.67 % 47.94 % 397 26.41 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 43.99 % 45.70 % 10.31 % 1078

Benchmark # Dets # Tracks
PEDESTRIAN 21290 649

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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