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Method

PointLink [la] [gp] [on] [PointLink]


Submitted on 10 Nov. 2026 08:40 by
Zixuan Wang (Shanghai Jiao Tong University)

Running time:0.042 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
PointLink is an online method for 3D multi-object
tracking in autonomous driving. It incorporates
image point tracking to capture temporal visual
information. This information supports object
association across frames. The method combines
camera and LiDAR observations. It uses only past
and current observations.
Parameters:
Point tracking: 25 points over 8 frames.
Second-stage association: IoU threshold 0.65, box
margin 0.
Latex Bibtex:

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 90.22 % 87.00 % 90.32 % 89.60 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.51 % 96.97 % 95.72 % 37239 1165 2164 10.47 % 43010 921

Benchmark MT PT ML IDS FRAG
CAR 84.62 % 14.00 % 1.38 % 35 330

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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