Method

Rethinking Joint Detection and Embedding for Multi-object Tracking in Multi-scenario [on] [MMTrack]


Submitted on 14 Jun. 2023 03:35 by
LB X (USST)

Running time:0.0135s
Environment:GPU

Method Description:
JDE-based MOT tracker
Parameters:
Latex Bibtex:
@ARTICLE{10462683,
author={Xu, Libin and Huang, Yingping},
journal={IEEE Transactions on Industrial
Informatics},
title={Rethinking Joint Detection and Embedding
for Multiobject Tracking in Multiscenario},
year={2024},
volume={},
number={},
pages={1-10},
doi={10.1109/TII.2024.3366983}}

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 49.28 % 44.08 % 55.33 % 47.15 % 72.98 % 58.79 % 77.59 % 79.26 %

Benchmark TP FP FN
PEDESTRIAN 14069 9081 886

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 56.19 % 75.34 % 56.95 % 175 41.20 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 30.93 % 36.43 % 32.65 % 457

Benchmark # Dets # Tracks
PEDESTRIAN 14955 359

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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