Method

StrongFusion-MOT [StrongFusion-MOT]


Submitted on 28 Jul. 2022 05:43 by
Xiyang Wang (Chongqing University (CQU SLAMMOT Team))

Running time:0.01 s
Environment:>8 cores @ 2.5 Ghz (Python + C/C++)

Method Description:
StrongFusion-MOT
Parameters:
TBD
Latex Bibtex:
@ARTICLE{9976946,
author={Wang, Xiyang and Fu, Chunyun and He,
Jiawei and Wang, Sujuan and Wang, Jianwen},
journal={IEEE Sensors Journal},
title={StrongFusionMOT: A Multi-Object Tracking
Method Based on LiDAR-Camera Fusion},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/JSEN.2022.3226490}}

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 43.42 % 38.86 % 48.83 % 45.30 % 53.81 % 52.54 % 63.17 % 70.53 %

Benchmark TP FP FN
PEDESTRIAN 14423 8727 5069

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 39.04 % 63.89 % 40.41 % 316 16.54 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 25.43 % 52.58 % 21.99 % 1574

Benchmark # Dets # Tracks
PEDESTRIAN 19492 848

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.


eXTReMe Tracker