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}
}

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