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 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 56.69 % 75.51 % 57.02 % 92.55 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 61.16 % 94.21 % 74.17 % 14284 878 9072 7.89 % 15972 396

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 31.62 % 35.74 % 32.65 % 76 522

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.


eXTReMe Tracker