Method

Parameterless LidarTracker [la] [SpbTracker]


Submitted on 10 Jul. 2024 09:08 by
Eunsoo Im (ajou)

Running time:0.07 s
Environment:2 cores @ 2.5 Ghz (Python + C/C++)

Method Description:
TBD, Lidar only
Parameters:
Parameterless
Latex Bibtex:
@article{im2024spb3dtracker,
title={Spb3DTracker: A Robust LiDAR-Based Person
Tracker for Noisy Environmen},
author={Im, Eunsoo and Jee, Changhyun and Lee,
Jung Kwon},
journal={arXiv preprint arXiv:2408.05940},
year={2024}
}

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 86.95 % 86.21 % 87.28 % 88.98 %
PEDESTRIAN 53.45 % 65.54 % 54.53 % 89.37 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.70 % 97.52 % 93.99 % 34175 870 3503 7.82 % 38750 1128
PEDESTRIAN 63.52 % 88.31 % 73.89 % 14897 1972 8554 17.73 % 18675 399

Benchmark MT PT ML IDS FRAG
CAR 74.92 % 20.31 % 4.77 % 116 544
PEDESTRIAN 31.62 % 40.21 % 28.18 % 250 1300

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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