Method

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

Submitted on 29 Jul. 2025 17:35 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.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={2025},
note={Unpublished}
}

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 91.84 % 86.88 % 91.93 % 89.55 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.39 % 98.53 % 96.41 % 37286 557 2218 5.01 % 43087 676

Benchmark MT PT ML IDS FRAG
CAR 87.38 % 4.62 % 8.00 % 30 54

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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