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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 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 81.12 % 78.33 % 84.65 % 83.28 % 85.77 % 87.38 % 91.50 % 88.09 %

Benchmark TP FP FN
CAR 32219 2173 1174

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.16 % 86.84 % 90.27 % 39 77.83 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.31 % 14.15 % 1.54 % 377

Benchmark # Dets # Tracks
CAR 33393 768

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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