Method

Boost Correlation Features with 3D-MiIoU-Based Camera-LiDAR Fusion for MODT in Autonomous Driving [BcMODT]


Submitted on 19 Aug. 2022 13:29 by
Elf Cheung (JLU)

Running time:0.01 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Our MOT system performs online joint object
detection and tracking, robust affinity computation
and comprehensive data association from paired
frames.
Parameters:
See the code for details.
Latex Bibtex:
@article{zhang2023boost,
title={Boost Correlation Features with 3D-MiIoU-
Based Camera-LiDAR Fusion for MODT in Autonomous
Driving},
author={Zhang, Kunpeng and Liu, Yanheng and Mei,
Fang and Jin, Jingyi and Wang, Yiming},
journal={Remote Sensing},
volume={15},
number={4},
pages={874},
year={2023},
publisher={Multidisciplinary Digital Publishing
Institute}
}

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
CAR 71.00 % 73.62 % 69.14 % 78.86 % 83.97 % 72.34 % 88.70 % 86.93 %

Benchmark TP FP FN
CAR 31039 3353 1260

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.48 % 85.31 % 86.59 % 381 72.22 %

Benchmark MT rate PT rate ML rate FRAG
CAR 78.15 % 19.23 % 2.62 % 732

Benchmark # Dets # Tracks
CAR 32299 1208

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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