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 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
CAR 86.53 % 85.37 % 86.66 % 88.29 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 91.50 % 96.65 % 94.00 % 35972 1248 3341 11.22 % 43069 1968

Benchmark MT PT ML IDS FRAG
CAR 78.31 % 19.08 % 2.62 % 45 626

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.


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