Method

LearnTrack-online [LearnTrack]
https://github.com/Still-Wang/LearnTrack

Submitted on 29 Jul. 2025 17:28 by
Haoyu Wang (Wuhan University)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We present LearnTrack, a learning-based approach
that optimizes Kalman filters for vehicle online
tracking using LiDAR data.The key innovation lies
in our optimization framework that considers
temporal motion patterns to handle non-linear
vehicle dynamics, addressing limitations of
traditional constant-noise Kalman filters.
Parameters:
TBD
Latex Bibtex:
@misc{whu_spacewang,
title={LearnTrack: Learning-based Kalman Filter
Optimization for Multi-Object Tracking},
author={Haoyu Wang},
year={2024},
note={Unpublished}
}

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 81.10 % 78.09 % 84.88 % 83.64 % 85.09 % 87.66 % 91.44 % 88.06 %

Benchmark TP FP FN
CAR 32371 2021 1434

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.82 % 86.81 % 89.95 % 45 77.40 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.23 % 11.54 % 1.23 % 419

Benchmark # Dets # Tracks
CAR 33805 917

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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