Method

Multi-Object Tracking and Segmentation via Neural Message Passing [MPNTrackSeg]
https://github.com/ocetintas/MPNTrackSeg

Submitted on 30 Jun. 2022 16:15 by
Orcun Cetintas (Technical University of Munich)

Running time:0.08 s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
This work builds upon our previous CVPR 2020
(oral) paper Learning a Neural Solver for Multiple
Object Tracking and extends it by:

- integrating an attentive module to our neural
message passing scheme to yield a unified model
for multi-object tracking and segmentation
- providing an extensive evaluation of our
tracking model over three challenging datasets,
including MOT20, KITTI and the recently proposed
Human in Events dataset.
Parameters:
All hyperparameters are specified in the code.
Latex Bibtex:
@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 55.50 % 60.45 % 52.04 % 64.67 % 75.15 % 59.76 % 70.45 % 79.29 %

Benchmark TP FP FN
PEDESTRIAN 16954 3743 857

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
PEDESTRIAN 76.99 % 75.95 % 77.78 % 162 57.29 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 56.30 % 34.07 % 9.63 % 720

Benchmark # Dets # Tracks
PEDESTRIAN 17811 294

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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