Method

Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking [AHMOT]
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Submitted on 8 Aug. 2024 11:26 by
J C (China University of Science and Technology)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This paper presents an enhanced 3D multiobject
tracking (MOT) framework designed for autonomous
vehicles operating in complex traffic
environments. The proposed framework includes
adaptive Kalman filtering (AKF) technology and
incorporating diagnostic and correction mechanisms
to improve tracking accuracy and system
robustness. The framework is designed to
dynamically adjust the filter parameters and adapt
to environmental changes, enhancing the stability
of state estimation. Furthermore, a 3D
hierarchical data association model is developed
that not only comprehensively considers the 3D
intersection-over-union (IoU), appearance, and
distance features of targets to improve data
association accuracy but also leverages a cascaded
strategy to increase real-time performance. By
utilizing an Internet of Things (IoT) framework to
integrate data from LiDAR and camera sensors, the
system compensates for the limitations of
individual sensors and enhances overall tracking
performance. [la][on]
Parameters:
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Latex Bibtex:
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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
PEDESTRIAN 51.63 % 50.30 % 53.37 % 58.85 % 62.70 % 58.64 % 70.28 % 75.79 %

Benchmark TP FP FN
PEDESTRIAN 17964 5186 3762

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 59.96 % 71.12 % 61.35 % 321 37.55 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 52.23 % 37.46 % 10.31 % 1035

Benchmark # Dets # Tracks
PEDESTRIAN 21726 727

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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