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 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 72.66 % 74.69 % 71.43 % 79.04 % 85.59 % 77.69 % 85.86 % 87.48 %
PEDESTRIAN 43.25 % 42.03 % 44.79 % 44.85 % 62.85 % 51.24 % 60.72 % 71.87 %

Benchmark TP FP FN
CAR 30884 3508 875
PEDESTRIAN 14559 8591 1961

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.51 % 86.07 % 87.26 % 257 74.00 %
PEDESTRIAN 53.55 % 65.28 % 54.42 % 200 31.72 %

Benchmark MT rate PT rate ML rate FRAG
CAR 74.77 % 20.46 % 4.77 % 496
PEDESTRIAN 31.61 % 40.21 % 28.18 % 1311

Benchmark # Dets # Tracks
CAR 31759 868
PEDESTRIAN 16520 312

This table as LaTeX

This figure as: png pdf

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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