Method

AGTrack: Adaptive Kalman Filtering and Geometric Similarity for 3D MOT [AGTrack]


Submitted on 28 Aug. 2026 07:56 by
wang weihan (吉林大学)

Running time:0.05 s
Environment:8 cores @ >3.5 Ghz (Python)

Method Description:
AGTrack is a novel 3D multi-object tracking
framework designed for autonomous driving
perception systems. It introduces a geometry-aware
3D Rotation-Shape IoU (3D-RSIoU) metric that
comprehensively models rotation alignment and
shape characteristics for more accurate data
association. Additionally, the framework
incorporates an adaptive Kalman filter that
dynamically adjusts noise parameters based on
real-time detection quality and prediction-
observation discrepancies, enhancing state
estimation robustness in complex motion scenarios.
The system operates within a single-stage matching
architecture, maintaining high efficiency while
achieving accuracy comparable to multi-stage
methods.
Parameters:
\lamda_1=1, \lamda_2=1, \lamda_3=0.5, lamda_4=0.5

Latex Bibtex:
@INPROCEEDINGS{11637544,
author={Zhang, Biao and Wang, Weihan and Yu, Kai
and Bi, Chengding and Sun, Yupeng and Lin, Ye and
Jia, Jiwei},
booktitle={2026 7th International Conference on
Computer Vision, Image and Deep Learning (CVIDL)},
title={AGTrack: A 3D Multi-Object Tracking
Framework With Adaptive Kalman Filtering and
Geometric Similarity Perception},
year={2026},
volume={},
number={},
pages={932-940},
keywords={Kalman filters;Signal
detection;Modeling;Noise;Object
tracking;Tracking;Measurement;Printing;Equations;C
onferences;3D Multi-Object Tracking;Adaptive
Kalman Filter;Geometric Similarity},
doi={10.1109/CVIDL70130.2026.11637544}}

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 82.75 % 79.38 % 86.90 % 82.97 % 87.40 % 89.94 % 91.34 % 88.10 %

Benchmark TP FP FN
CAR 32105 2287 541

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.75 % 86.88 % 91.78 % 9 79.50 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.23 % 4.77 % 8.00 % 54

Benchmark # Dets # Tracks
CAR 32646 631

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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