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note={Unpublished}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

Benchmark TP FP FN

Benchmark MOTA MOTP MODA IDSW sMOTA

Benchmark MT rate PT rate ML rate FRAG

Benchmark # Dets # Tracks

This table as LaTeX

[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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